{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples and Exercises from Think Stats, 2nd Edition\n",
    "\n",
    "http://thinkstats2.com\n",
    "\n",
    "Copyright 2016 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import nsfg\n",
    "import first\n",
    "import analytic\n",
    "\n",
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential distribution\n",
    "\n",
    "Here's what the exponential CDF looks like with a range of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(3)\n",
    "for lam in [2.0, 1, 0.5]:\n",
    "    xs, ps = thinkstats2.RenderExpoCdf(lam, 0, 3.0, 50)\n",
    "    label = r'$\\lambda=%g$' % lam\n",
    "    thinkplot.Plot(xs, ps, label=label)\n",
    "    \n",
    "thinkplot.Config(title='Exponential CDF', xlabel='x', ylabel='CDF', \n",
    "                 loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of interarrival times from a dataset of birth times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = analytic.ReadBabyBoom()\n",
    "diffs = df.minutes.diff()\n",
    "cdf = thinkstats2.Cdf(diffs, label='actual')\n",
    "\n",
    "thinkplot.Cdf(cdf)\n",
    "thinkplot.Config(xlabel='Time between births (minutes)', ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the CCDF looks like on a log-y scale.  A straight line is consistent with an exponential distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdf(cdf, complement=True)\n",
    "thinkplot.Config(xlabel='Time between births (minutes)',\n",
    "                 ylabel='CCDF', yscale='log', loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normal distribution\n",
    "\n",
    "Here's what the normal CDF looks like with a range of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(3)\n",
    "\n",
    "mus = [1.0, 2.0, 3.0]\n",
    "sigmas = [0.5, 0.4, 0.3]\n",
    "for mu, sigma in zip(mus, sigmas):\n",
    "    xs, ps = thinkstats2.RenderNormalCdf(mu=mu, sigma=sigma, \n",
    "                                               low=-1.0, high=4.0)\n",
    "    label = r'$\\mu=%g$, $\\sigma=%g$' % (mu, sigma)\n",
    "    thinkplot.Plot(xs, ps, label=label)\n",
    "\n",
    "thinkplot.Config(title='Normal CDF', xlabel='x', ylabel='CDF',\n",
    "                 loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll use a normal model to fit the distribution of birth weights from the NSFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "preg = nsfg.ReadFemPreg()\n",
    "weights = preg.totalwgt_lb.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the observed CDF and the model.  The model fits the data well except in the left tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean, Var 7.280883100022579 1.5452125703544872\n",
      "Sigma 1.2430657948614334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# estimate parameters: trimming outliers yields a better fit\n",
    "mu, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "print('Mean, Var', mu, var)\n",
    "    \n",
    "# plot the model\n",
    "sigma = np.sqrt(var)\n",
    "print('Sigma', sigma)\n",
    "xs, ps = thinkstats2.RenderNormalCdf(mu, sigma, low=0, high=12.5)\n",
    "\n",
    "thinkplot.Plot(xs, ps, label='model', color='0.6')\n",
    "\n",
    "# plot the data\n",
    "cdf = thinkstats2.Cdf(weights, label='data')\n",
    "\n",
    "thinkplot.PrePlot(1)\n",
    "thinkplot.Cdf(cdf) \n",
    "thinkplot.Config(title='Birth weights',\n",
    "                 xlabel='Birth weight (pounds)',\n",
    "                 ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A normal probability plot is a visual test for normality.  The following example shows that if the data are actually from a normal distribution, the plot is approximately straight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1000\n",
    "thinkplot.PrePlot(3) \n",
    "\n",
    "mus = [0, 1, 5]\n",
    "sigmas = [1, 1, 2]\n",
    "\n",
    "for mu, sigma in zip(mus, sigmas):\n",
    "    sample = np.random.normal(mu, sigma, n)\n",
    "    xs, ys = thinkstats2.NormalProbability(sample)\n",
    "    label = '$\\mu=%d$, $\\sigma=%d$' % (mu, sigma)\n",
    "    thinkplot.Plot(xs, ys, label=label)\n",
    "\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='standard normal sample',\n",
    "                 ylabel='sample values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the normal probability plot for birth weights, showing that the lightest babies are lighter than we expect from the normal mode, and the heaviest babies are heavier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "std = np.sqrt(var)\n",
    "\n",
    "xs = [-4, 4]\n",
    "fxs, fys = thinkstats2.FitLine(xs, mean, std)\n",
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "xs, ys = thinkstats2.NormalProbability(weights)\n",
    "thinkplot.Plot(xs, ys, label='all live')\n",
    "\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='Birth weight (lbs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we suspect that the deviation in the left tail is due to preterm babies, we can check by selecting only full term births."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_term = preg[preg.prglngth >= 37]\n",
    "term_weights = full_term.totalwgt_lb.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the deviation in the left tail is almost gone, but the heaviest babies are still heavy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "std = np.sqrt(var)\n",
    "\n",
    "xs = [-4, 4]\n",
    "fxs, fys = thinkstats2.FitLine(xs, mean, std)\n",
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "thinkplot.PrePlot(2) \n",
    "xs, ys = thinkstats2.NormalProbability(weights)\n",
    "thinkplot.Plot(xs, ys, label='all live')\n",
    "\n",
    "xs, ys = thinkstats2.NormalProbability(term_weights)\n",
    "thinkplot.Plot(xs, ys, label='full term')\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='Birth weight (lbs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lognormal model\n",
    "\n",
    "As an example of a lognormal disrtribution, we'll look at adult weights from the BRFSS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brfss\n",
    "df = brfss.ReadBrfss()\n",
    "weights = df.wtkg2.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function estimates the parameters of a normal distribution and plots the data and a normal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeNormalModel(weights):\n",
    "    \"\"\"Plots a CDF with a Normal model.\n",
    "\n",
    "    weights: sequence\n",
    "    \"\"\"\n",
    "    cdf = thinkstats2.Cdf(weights, label='weights')\n",
    "\n",
    "    mean, var = thinkstats2.TrimmedMeanVar(weights)\n",
    "    std = np.sqrt(var)\n",
    "    print('n, mean, std', len(weights), mean, std)\n",
    "\n",
    "    xmin = mean - 4 * std\n",
    "    xmax = mean + 4 * std\n",
    "\n",
    "    xs, ps = thinkstats2.RenderNormalCdf(mean, std, xmin, xmax)\n",
    "    thinkplot.Plot(xs, ps, label='model', linewidth=4, color='0.8')\n",
    "    thinkplot.Cdf(cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of adult weights and a normal model, which is not a very good fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n, mean, std 398484 78.59599565702814 17.75455519179871\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalModel(weights)\n",
    "thinkplot.Config(title='Adult weight, linear scale', xlabel='Weight (kg)',\n",
    "                 ylabel='CDF', loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of adult weight and a lognormal model, plotted on a log-x scale.  The model is a better fit for the data, although the heaviest people are heavier than the model expects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n, mean, std 398484 1.884660713731975 0.09623580259151371\n"
     ]
    },
    {
     "data": {
      "image/png": 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IlBh35913382606itPUl1dU26H4JKCP1v6tSpvPjiizQ0NFBVVcXBBx9MbW0tzz77LCeffDKvv/46hx9+OBAk7ZkzZ2a9/oUXXuDoo49ml112AeD000/nz3/+c3r7KaecQiwWY9KkSbz77rt5Y5g5cybf+ta3qKur49RTT2Wfffbp9+tUUhApMZkT43T2RaisrEr3VFbHtGhUVFQwfvx4fv7zn3PYYYcxefJknnrqKf76178yYcIEjjvuOO6+++5uX9/bOHOZQ3J3t+9ZZ53FjBkzWLJkCZ/+9Ke55ZZb0tVY/UVJQaSEtLa28sEHH6SXg2GvoaoqeCuXS6NyoRLfUUcdxfXXX8+tt97KAQccwOWXX87UqVM59NBD+cd//EfWrFnDxIkTaWpqoq6ujn333Tf92unTp3PZZZexceNGhg8fzv3335+erKc7w4cPzxrhdu3atey9995ccsklrF27lldffVVJQaRcuTvvvfde1rr2ZAc1NTWYqcpoIBx55JF861vfYubMmQwdOpTq6mqOPPJIxowZw2233cb8+fPTpbhrr702KynsvvvufPWrX2XGjBl89KMfZdKkSYwYMaLH802ePJlEIsGBBx7IueeeS0tLC7/4xS+oqKhgt912y2qz6C8aOlukRGzatIkNGzZkDYFdVVWVnulrsFcbDYahsxsbGxk2bBjJZJJ58+Zx/vnnM2/evH4/z44Mna1bUkVKQDKZpL6+Htg6BHY8nkgnhHKpNip111xzDVOmTOGTn/wkEyZM4JRTTil0SF2o+kikBNTX16eHsWhrD3ovV1ZWAqo2KiXXX399oUPolZKCSJFrbW3tMp1mZWUlsZgN+iqjXO6evu1W8tvRJgFVH4kUMXfn/fffz1oXi8VIJMqvuqi6upr6+vod/tAbzNyd+vp6qqurt/sYKimIFLGmpiaam5uBrX0SMudGKCfjxo2jrq4uqye3dFVdXc24ceO2+/VKCiJFqvNbX6egT4JTXRkHyq9xuaKiggkTJhQ6jEFP1UciRaqxsZG2tjYgKCW0tSepqFDjskRLJQWRIuTufPDBB1l9EhKJBPF4bFAPdCeFp5KCSBFqaGigvb09nRCA9KQ5KiFIlFRSECky7s7GjRuBrX0SEokENdVVmlJTIqekIFJktmzZQnt7e9a6igpVGcnAUPWRSBHJLCV0SiQSxGJ6q8rAUElBpIg0NTXR2tqabmCGrW0JIgMh0q8fZnaCmb1hZmvM7Mo82/c0s6fM7Pdm9qqZnRRlPCLF7sMPP+SJlWt48KnX0oPexWKxsuuTIIUTWVIwszhwM3AiMAmYb2aTcnb7T+CX7n4QcCbw46jiESl2ra2tNDc3p0sIABUVCfVJkAEVZfXRdGCNu68FMLN7gLnA6xn7OLBT+HwE8HaE8YgUtc5SQucdR7FYnPNOPUJ3G8mAirL6aHfgrYzlunBdpmuAz5tZHbAU+Eq+A5nZRWZWa2a1GvdEBqNkMslDj/+eB596Lb1u+NAaJQQZcFEmhXxDduUObzgfuM3dxwEnAf9tZl1icveF7j7N3aeNGTMmglBFCmvTpk1Z1UZmMeZ/ZkYBI5JyFWVSqAP2yFgeR9fqoQuAXwK4+/NANTA6wphEio67s3nz5nS1EcCCuTM4ZfaUAkYl5SrKpLAK2MfMJphZJUFD8uKcff4GzAYws08QJAXVD0lZaWxspKOjI2ONccZJhxYsHilvkSUFd08CFwOPAasJ7jJ6zcy+YWYnh7tdAVxoZq8AdwPnumbQkDKzadOmrGV1VpNCirTzmrsvJWhAzlx3Vcbz14HDo4xBpJi1trbS0tKSta6iQn1KpXD0dUSkgHJLCbFYXKUEKSj994kUSCqVorGxMWudSglSaEoKIgXS2NhIKpVKL5sZ8biSghSWkoJIgWzevDlrOZFIYPl694gMIH0tESmAzAbmzhFREwkNeieFp5KCSAE0NDQApEdETXZ4uoFZI6JKISkpiAwwd08nha1zJgSFdo2IKoWm6iORAdbU1JTuwRwMbWHE43EWzJ2pAfCk4FRSEBlgXRuY45iZEoIUBSUFkQHU0dFBU1MTELQnQHDXkUix0H+jyABqaGjg8RV/YcnyN2hrT2IWIxaLq3FZioZKCiIDqLGxMZ0QoLPqCDUuS9FQSUFkgLS1tdHS0pI1b8KwITWcOWe62hOkaCgpiAyQ3HGOYrE4d99wYYGiEclP1UciAyCzb0KnRCJeoGhEuqekIDIAWltbaW9vz1qnu46kGCkpiAyA3FJCPB70TRApNkoKIhFz93R7gvomSLFTUhCJWEtLCx0dHenB7yAoKahvghQjJQWRiDU0NGQlhGDeBFPfBClKSgoiEeqsOuocDRUgHk9o8DspWkoKIhFqamoilUpldFgzzjv1cCUEKVpKCiIRyu2wlkjEOWX2lAJFI9I7JQWRiLg7W7ZsyVqnu46k2CkpiESks+qo8zZUMyMWUy9mKW762iISkfseq+X+376cbk+Ix+NUV+k2VCluKimIRMDdeeDxl7NGRE0kEroNVYqekoJIBJqammht25oQqiorOHee7jqS4qfqI5EI5DYw/9c1ZzJ27NgCRSPSdyopiPSzzLGOOg0bNqxA0Yhsm0iTgpmdYGZvmNkaM7uym33+zsxeN7PXzOyuKOMRGQjNzc2kUqmMNUZNTU3B4hHZFpFVH5lZHLgZOA6oA1aZ2WJ3fz1jn32AfwcOd/eNZqbytZS8rn0TNEy2lI4oSwrTgTXuvtbd24B7gLk5+1wI3OzuGwHc/b0I4xGJXL6qo3hcTXdSOqJMCrsDb2Us14XrMu0L7Gtmz5nZCjM7Id+BzOwiM6s1s9oNGzZEFK7IjuscJnsrIx5XhzUpHVEmhXzlZc9ZTgD7ALOA+cAtZrZzlxe5L3T3ae4+bcyYMf0eqEh/ya06CmZYK1AwItshyqRQB+yRsTwOeDvPPg+7e7u7/w/wBkGSECk5+WdYUylBSkuUSWEVsI+ZTTCzSuBMYHHOPg8BxwCY2WiC6qS1EcYkEpm2tjaSyaRmWJOSFllScPckcDHwGLAa+KW7v2Zm3zCzk8PdHgPqzex14CngX9y9PqqYRKLU2NjYJSFohjUpNZHeFuHuS4GlOeuuynjuwOXhQ6SkbdmyRTOsSclTj2aRftDW1hY8MgbAO+/Uw5QQpOQoKYj0g9y7jmKxOPM+dXCBohHZfkoKIv0gXy9mkVKkpCCyg5LJJC0tLVnr1ItZSpX+c0V20JYtW3hi5Zp0I3MsFiMWU481KU0qKYjsoMbGRpYsfyNj2s2E+iZIyVJSENkBHR0dPPL0H7LuOho6pFp9E6Rk9ZgUzOy2jOfnRB6NSIlpamrK6ptQXVXBPTdcqFtRpWT1VlLI/M++NMpARErRli1bskoJpx2v21CltPWWFHJHNRWRUCqV6nIr6mmfVrWRlLbe7j4aZ2Y3EQyD3fk8zd0viSwykSLX3NxMMFJLwMyoqqoqYEQiO663pPAvGc9rowxEpNR0nTshoWk3peT1mBTc/faBCkSklLi7ejHLoNTrLalmdo6ZvWRmW8JHrZktGIjgRIpVvmk3YzElBSl9PZYUwg//fyIY2volgraFg4Hvmxnufkf0IYoUn9xezJp2UwaL3koKXwbmuftT7r7J3T909yeBz4XbRMpOZ9VRZi/mREIzrMng0FtD807uvi53pbuvM7OdoglJpLg98NsXuf3B57J7MdeoF7MMDr0lhebt3CYyaN29ZFVWQhhSXcVd13+xgBGJ9J/eksInzOzVPOsN2DuCeESKXlPz1mGyKysSnH6CejHL4NFbUjgQ2BV4K2f9XsDbkUQkUsSSySSpVCq9fOMVcxg/fnzhAhLpZ701NP8A2Ozub2Y+gKZwm0hZaWxszFqurq4mkdC0JDJ49JYUxrt7l+ojd68FxkcSkUgRy+2wNnTo0AJFIhKN3pJCdQ/bavozEJFi19HRQXNz9v0VSgoy2PSWFFaZ2YW5K83sAuDFaEISKU73PvoCl9+wJL0ci8WorKwsYEQi/a+3ytB/Ah40s7PZmgSmAZXAvCgDEykmDz/5Cv/9q5VZQ1sMqdGIqDL49DYg3rvAYWZ2DPDJcPWSsFezSNlYtHRVVkKorEhw5omHFDAikWj06bYJd38KeCriWESK1paMvgnzjtmfTx++H3vttVcBIxKJRq+jpIoIWaWE2TMmMnToUM2dIIOSkoJIL9ydZLIja92wYcMKFI1ItJQURHoR3Ia6ddrNeDxOdXVPd2uLlC4lBZFe5OuwpqojGawiTQpmdoKZvWFma8zsyh72O83M3Mw09rAUFXfvMrSFOqzJYBZZUjCzOHAzcCIwCZhvZpPy7DccuARYGVUsItsr37SbNTXqzC+DV5QlhenAGndf6+5twD3A3Dz7fRP4HtCSZ5tIQeVWHcXjcWIx1brK4BXlf/fuZA+5XReuSzOzg4A93P2Rng5kZheZWa2Z1W7YsKH/IxXJw9158PHfZw1tkUjECxiRSPSiTAr5WuLSt3CYWYxg+O0rejuQuy9092nuPm3MmDH9GKJI91pbW/nVM693mXZTZDCLMinUAXtkLI8je2Ke4QRDZzxtZuuAQ4HFamyWYnHfY7XZ027WVHHGSRraQga3KGcHWQXsY2YTgP8FzgTO6tzo7puA0Z3LZvY08M/hXA0iBfXQEy9z5yOr0suVFQluv+4chg8fXsCoRKIXWUnB3ZPAxcBjwGrgl+7+mpl9w8xOjuq8Iv3hnqUv4L512s05R+7HkCFDChiRyMCIdB5Bd18KLM1Zd1U3+86KMhaRbbGlKXsAvM/OOoB4XI3MMvjp3jqRHLljHc2eMVFjHUnZUFIQydHW1pZVdWRm6sUsZSPS6iORUvPwk6/w3w8/l7WupqZGVUdSNpQUREIPP/kKtz/0PM3Nrel1lRUJVR1JWVH1kUho0aO1pFKpdNVRZUWCOUfup6ojKSsqKYiEWtva6egIOqvNO2Z/Zs+YyJAhQ1R1JGVFJQWRkDskk0FSmD1jIoA6q0nZUVIQCaVSHbhvnWFNdx1JOVJSEAllz5sAQ4YM0TDZUnb0Hy9CZ4e1ZNY6VR1JOVJSEAGam5u7VB1prCMpR0oKItBlHuZhw4ap6kjKkv7rpeylUqm8SUGkHCkpSNlramrit8//Ob2sqiMpZ0oKUvbue2wVDz71Wnp5SE01ZvlmkxUZ/JQUpKw9+PhL3PPoS1nr5mvKTSljSgpSth5+8hV+/kD2iKinHTeZz31a04RL+VJSkLK16NHarL4J847Zn1OPm6qqIylrSgpStppbWkmlghFROwfAU4c1KXdKClK2MksJs2dMpKamhoqKigJGJFJ4SgpSlvINa7HTTjsVKBqR4qH5FKTsPPzkK9z1qxVZw1rEYjGNiCqCSgpShhY9WktjU3N6uXPKTQ1rIaKSgpSRh598hUWP1tLc0pYeJruyIsGcIz6uqiORkJKClI1Fj9bS2tZOMtkOBAnhxivmUFlZSVVVVYGjEykOKi9L2Whta09PudlZQgAYMWKE+iaIhFRSkLLS0RFMuXnjFXOAYPA7jYgqspVKClJWOquOOg0bNox4PF6gaESKj5KClI1UKtVlHuYRI0YUKBqR4qSkIGUjt7NaVVUV1dXVBYpGpDhFmhTM7AQze8PM1pjZlXm2X25mr5vZq2b2hJntFWU8Ur5SqRTt7dlJQaUEka4iSwpmFgduBk4EJgHzzWxSzm6/B6a5+2TgPuB7UcUj5W3R0hVAdg9mNTCLdBVlSWE6sMbd17p7G3APMDdzB3d/yt2bwsUVwLgI45Ey9dATL3PHwyvTy5UVCUaMGKEezCJ5RPmu2B14K2O5LlzXnQuAR/NtMLOLzKzWzGo3bNjQjyFKObj7kZW4p9LLc47cT1VHIt2Isp9Cvt5AnmcdZvZ5YBpwdL7t7r4QWAgwbdq0vMcQydU5rEXDlq3jHM07Zn9OmT2FREJddETyifKdUQfskbE8Dng7dycz+xTwH8DR7t4aYTxSZhY9WktTcwup1NZxjmbPmMjOO+9c4MhEileU1UergH3MbIKZVQJnAoszdzCzg4CfAie7+3sRxiJlqLWtnfb2reMczTni49TU1GicI5EeRFZScPekmV0MPAbEgVvd/TUz+wbaCHHUAAANkklEQVRQ6+6Lge8Dw4B7w7Fn/ubuJ0cVk5SXjo6tndU6h7UYOXJkIUMSKXqRVqy6+1Jgac66qzKefyrK80t56ywldKqurqampqZA0YiUBt2TJ4PSfY/V0tGR3Vlt5MiRGg1VpBe6BUMGlc47jjZtbkivq6xIUFlZyZAhQwoYmUhpUElBBpXOO44yB76bc8THGTVqlEoJIn2gkoIMGg8/+UreO45OOmp/lRJE+khJQQaNRY/W0tHRQUdHR3qqTUClBJFtoOojGTRaWttpa2sDSE+1WVNTozuORLaBkoIMGslkklQqGONo9oyJgEoJIttKSUEGhY6ODtrb27LWDR8+XJPoiGwjJQUZFO5a/BzuW8dKNDN22WWXAkYkUpqUFKTk3ffrVdy5ZFV6ubIiwciRI6moqChgVCKlSUlBStpDT7zMrQ8sz1p38qxJGglVZDvpllQpSem5Ehq3pBuXIZgv4azPHq5Z1US2k945UpI6ey5nDno375j9OeVTB6mjmsgOUElBSlJLaxutrcHdRp09l48/7OOMHj26wJGJlDYlBSk57k5ra1t63uXOnstjx44lHo8XMjSRkqfqIyk59yx5vsuw2DvttBNDhw4tUEQig4eSgpSUe3/9Arc/9Hx6uXNYbFUbifQPVR9JSXj4yVe4Z8kLbNzUkLV+zpH7sdtuu+luI5F+oneSlIR7lr7Ah5sbgK29lucdsz9nn3w4lZWVhQtMZJBRSUGKXkdHB5s2N6aHsei82+j0E6czbNiwAkcnMrgoKUhRSyaTvP3221kd1G68Yg7Dhw9n5MiRBYxMZHBSUpCi1d7ezu0PPM3ip1/PWj906FDGjh2rIbFFIqCkIEWpubmZXzz0LPc/8Yes9TXVVey6665KCCIRUVKQouLu3P3I8yxauorW9uy+CEOqq1hwisY1EomSkoIUjWQyyXvvvcc9S1fRlpMQ5p84lQWnHq0SgkjElBSk4NydzZs3s2jpCh5Z9qeshFBZkeCME6dy1mcPV0IQGQBKClIw7s4vH13J3UtW0dLa1mV7VWWCO793gW47FRlASgoy4FKpFI2NjXz44Yfc+auVXaqKAGqqK/nCyYcpIYgMMCUFGRDuTktLC42NjTQ0NPDb5//MkuVvdEkIVRUJzjjpEOZ/Zqaqi0QKQElBIuHutLe309LSQlNTE01NTd0mAgjaDv7rmjMZNWoUiYT+LUUKRe8+2SHuTjKZpL29Pf1obW2ltbU13Qv5iZVruk0GAENqqjnrMzPYddddBzJ0Eckj0qRgZicAPwTiwC3u/p2c7VXAHcBUoB44w93XRRlTqevo6EiPAdQXuft2Luf7me+RSqXSP1OpFB0dHV0e+fSWCMyMoWEyOPX4qX2+HhGJVmRJwcziwM3AcUAdsMrMFrt75pgFFwAb3X2imZ0JfBc4I6qYBoO6urqseYkHSm8f8n1jDK2p4owTp3H6idPVZiBShKIsKUwH1rj7WgAzuweYC2QmhbnANeHz+4AfmZn5tnwV7qPPXfqT/j5kQTQ1NaenoSx+RiwWIx6PMaSmmvlzpnPK7CmFDkpEehBlUtgdeCtjuQ6Y0d0+7p40s03AKOD9zJ3M7CLgIoA999wzqnhlO5lZ+IgRiwWJIBaLUVNdyRknHsLcYw8sdIgi0kdRJoV8dQO5JYC+7IO7LwQWAkybNq3fSxGlJKhx2d5ql62vy19zYxnrLX2uznXVVRV87riD+OysA4jH48TjcRKJhKqBRAaRKJNCHbBHxvI44O1u9qkzswQwAvggimDu/+GXojisiMigEuVwk6uAfcxsgplVAmcCi3P2WQycEz4/DXgyivYEERHpm8hKCmEbwcXAYwS3pN7q7q+Z2TeAWndfDPwM+G8zW0NQQjgzqnhERKR3kfZTcPelwNKcdVdlPG8BTo8yBhER6TvNViIiImlKCiIikqakICIiaUoKIiKSZqV2B6iZbQDeLHQcvRhNTq/sEqZrKU66luJVrNezl7uP6W2nkksKpcDMat19WqHj6A+6luKkaylepX49qj4SEZE0JQUREUlTUojGwkIH0I90LcVJ11K8Svp61KYgIiJpKimIiEiakoKIiKQpKWwnM7vVzN4zsz/2st8hZtZhZqcNVGzbqi/XYmazzOxlM3vNzJ4ZyPi2RW/XYmYjzOxXZvZKeC3nDXSMfWVme5jZU2a2Ooz10jz7mJndZGZrzOxVMzu4ELH2po/XcnZ4Da+a2e/MrCin7OvLtWTsW/Tv/y7cXY/teABHAQcDf+xhnzjwJMFIsacVOubtvRZgZ4K5tfcMl8cWOuYduJavAt8Nn48hGLK9stBxdxPrR4CDw+fDgT8Dk3L2OQl4lGBavUOBlYWOeweu5TBgZPj8xFK+lnBbSbz/cx8qKWwnd19G77PEfQW4H3gv+oi2Xx+u5SzgAXf/W7h/0V5PH67FgeEWzCE6LNw3ORCxbSt3X+/uL4XPG4DVBPOaZ5oL3OGBFcDOZvaRAQ61V325Fnf/nbtvDBdXEMzWWHT6+HeBEnn/51JSiIiZ7Q7MA35S6Fj6wb7ASDN72sxeNLMFhQ5oB/wI+ATB1LB/AC5191RhQ+qdmY0HDgJW5mzaHXgrY7mO/B9QRaOHa8l0AUEJqKh1dy2l/P6PdJKdMvd/gH9z945BMLF9ApgKzAZqgOfNbIW7/7mwYW2XTwMvA8cCHwN+a2bPuvvmwobVPTMbRvCN85/yxJnvn6to7zPv5Vo69zmGICkcMZCxbaterqVk3/9KCtGZBtwT/kOMBk4ys6S7P1TYsLZLHfC+u28BtpjZMuBAgrrUUnMe8B0PKn3XmNn/APsBLxQ2rPzMrILgg+dOd38gzy51wB4Zy+MISkFFpw/XgplNBm4BTnT3+oGMb1v04VpK9v2v6qOIuPsEdx/v7uOB+4Avl8I/RDceBo40s4SZDQFmENSjlqK/EZR4MLNdgY8DawsaUTfCdo+fAavd/cZudlsMLAjvQjoU2OTu6wcsyD7qy7WY2Z7AA8AXirkU2pdrKeX3v0oK28nM7gZmAaPNrA64GqgAcPeSqkfs7VrcfbWZ/Rp4FUgBt7h7j7fiFkof/i7fBG4zsz8QVL38m7sX4zDHAIcDXwD+YGYvh+u+CuwJ6etZSnAH0hqgiaAkVIz6ci1XAaOAH4ffsJNenKON9uVaSpaGuRARkTRVH4mISJqSgoiIpCkpiIhImpKCiIikKSmIiEiakoIULTP7gZn9U8byY2Z2S8byDWZ2eS/H+F0fzrPOzEbnWT/LzA7r4XWnmNlV4fNrzOyfeztXN8cZFY662WhmP8rZNtXM/hCOgnqT5ekea2a3bcsonGb2GTP7+vbEKoOfkoIUs98RjJyJmcUIeobun7H9MOC5ng7g7t1+qPfBrM7zd+NfgR/vwPE7tQBfA/Illf8HXATsEz5O6IfzLQFODjsiimRRUpBi9hxbP5T3B/4INJjZSDOrIhjY7vcAZvYvZrYqHIs//S3YzBrDnzEz+3E4/v0jZrY059v1V8zspfBb+X7hQGdfAi6zYB6JIzMDM7N9gdZ8Hd/MbIqZrQhjedDMRobrDwnXPW9m37dwzgd33+LuywmSQ+ZxPgLs5O7Ph8Ny3AGc0tMvzMy+GZYcYmZ2kpn9ycyWh6WMR8LzOfA08JmejiXlSUlBipa7vw0kw+EPDgOeJxiNcibB2DKvunubmR1P8C16OjAFmGpmR+Uc7lRgPHAA8MXwGJned/eDCb6Z/7O7ryMY4fIH7j7F3Z/N2f9w4KVuQr+DoKf0ZIKRWK8O1/8c+JK7zwQ6+vAr2J1gbKNOPY6AambfA8YS9GquBH5KMIbQEQRzR2SqBY5EJIeSghS7ztJCZ1J4PmO5s73g+PDxe4IP6v0IkkSmI4B73T3l7u8AT+Vs7xzU7EWC5NGbjwAbclea2QhgZ3fvnJ3uduAoM9sZGO7unTHf1YdzbMsIqF8Lz/v3YUlgP2Ctu/9PuP3unP3fAz7ahxikzGjsIyl2ne0KBxBUH70FXAFsBm4N9zHg2+7+0x6O09v4xa3hzw769r5oBkb0Yb++nj+fOrInmulpBNRVBCWkXdz9gz6cr5rgGkSyqKQgxe45grrvD9y9I/zA25mg+uf5cJ/HgPPD8e0xs93NbGzOcZYDnwvr2nclaETuTQPBdIv5rAYm5q50903Axow2iC8Az4QzijWEI5kCnNnbycPRThvM7NDwrqMFBCPW5vNr4DvAEjMbDvwJ2DtsGwE4I2f/fQmSrEgWJQUpdn8guOtoRc66TZ2NvO7+G4LqmOfD0U/vo+uH+f0E37z/SFDXvhLY1Mu5fwXMy9fQDCwDDsp3iyhwDvB9M3uVoI3jG+H6C4CFZvY8wTf59PnNbB1wI3CumdWZ2aRw0z8QzC+wBvgrPcxG5u73Av9FMJw2wJeBX5vZcuDdnOs9huAuJJEsGiVVyoaZDXP3RjMbRTCpzuFh+8L2Hu+HwK/c/fFtOX/4/ErgI+5+6faev6/nCxPXzcBf3P0HYUnpLnefHdW5pXSpTUHKySNhg28l8M0dSQih6wgmHOqrOWb27wTvuzeBc3fw/L250MzOIbje3xOUkCAY9/+KiM8tJUolBRERSVObgoiIpCkpiIhImpKCiIikKSmIiEiakoKIiKT9fxZBlLgSiF2HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_weights = np.log10(weights)\n",
    "MakeNormalModel(log_weights)\n",
    "thinkplot.Config(title='Adult weight, log scale', xlabel='Weight (log10 kg)',\n",
    "                 ylabel='CDF', loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function generates a normal probability plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeNormalPlot(weights):\n",
    "    \"\"\"Generates a normal probability plot of birth weights.\n",
    "\n",
    "    weights: sequence\n",
    "    \"\"\"\n",
    "    mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "    std = np.sqrt(var)\n",
    "\n",
    "    xs = [-5, 5]\n",
    "    xs, ys = thinkstats2.FitLine(xs, mean, std)\n",
    "    thinkplot.Plot(xs, ys, color='0.8', label='model')\n",
    "\n",
    "    xs, ys = thinkstats2.NormalProbability(weights)\n",
    "    thinkplot.Plot(xs, ys, label='weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we generate a normal probability plot with adult weights, we can see clearly that the data deviate from the model systematically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalPlot(weights)\n",
    "thinkplot.Config(title='Adult weight, normal plot', xlabel='Weight (kg)',\n",
    "                 ylabel='CDF', loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we make a normal probability plot with log weights, the model fit the data well except in the tails, where the heaviest people exceed expectations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalPlot(log_weights)\n",
    "thinkplot.Config(title='Adult weight, lognormal plot', xlabel='Weight (log10 kg)',\n",
    "                 ylabel='CDF', loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pareto distribution\n",
    "\n",
    "Here's what the Pareto CDF looks like with a range of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O8f4oxwfG6R6I0j0wTuwc2/k9bqGtzk97XYC2eue5tc5PW52fxpDPRuGYqmZBoYx2P/T8hIBw2Y61fObDN9hmNHMQT6Y5PhDlSF+EI2ciHOsf59RQ7JxG9tQFPaxsCLKyIcDKhgDt9QFW1AdorvHZr31jZmClU5n8Ys9rfPMHj+fPL9uxlj/6yNtsnaJpqCqnhmK83jvG4d4Ih/sinBiMMtc5W2G/m47GIJ1NIVY1BljVGGRVQ5CagP3vbcy5sn81ZfDSgRN85dsP58+3rl/BZz58gwWErEQqw+G+CAd7xjhwepRDvZE5rb0jAm11ftY0h+lsDrK6KcTqpiD1Ia/98jemRCwolNjwaJS//uYDZDJOO/fqlU18/vffUdVNRolUhtd7x9h/apT9p0Y53BshNUs1QARW1AdY0xJiXWuYruYQXc0h/DZ015iyqt6SqgxUlb/7twcZGnVWOK2rCfK/f+zmqutUVlWOnBnnlRMjvHxyhIM9Y7MurVwb8LChvYb1rWHWtYVZ2xIm6LMAYMxCs6BQQrsfeoFnXzmeP//DD7yF5obqWNl0LJbipe5hXjw+zL4TI4zNMhegvd7P5hW1bGyvYdOKGlpr/dYEZMwiYEGhRA4e7eVbP3oyf37bWy/iku1dFcxR+fUMx3j26BDPHR3i9d6xs44MWtEQYOvKWrZkH3VBW+zPmMXIgkIJZDIZ/vF7j+T7ETZ2tfH+Wy6vcK5KT1U5MRhl76FBnj4yyKmh2IxpawMeLuis44KOerauqqUxbMt/G7MUWFAogQef3M+h430AeD1uPv3B65fVzminh2I88Xo/ew4N0DMcnzaNCKxvDfOGrgYuXF3P6qagNQcZswRZUJinSDQ+odnoXTfsZGVrfQVzVBqjsSRPvT7A4wf7OdI3Pm0ar1vY0VnPzjVOILAmIWOWPgsK8/T//mQvoxGnGaW5Icy7r99Z4Rydv0xG2XdihEf29/HCseFph436vS4uWt3Apesa2bG6Dv8yqhEZYywozMuxUwP85Jcv5c8/9K6r8PuW3q/lkWiSR/ef4Rev9tE/lphy3+MSLlxdzxUbm3jD6gZb39+YZcyCwjx858dPkckOublg4yqu2rm+wjk6N0fPRLj/pR72Hhqctlawvi3M1Zta2LW+kbDf/lcxphqU9V+6iNwEfBlwA19T1S9Out8FfBNoyKb5nKreW848lcqJ3iH2vHgkf/6R91y1JDpWVZUXjg9z3wunOXB6bMr9sN/N1ZtbuGZzC6sagxXIoTGmksoWFETEDXwVuBHoBvaIyG5Vfbko2Z8A31XVvxeR7cC9wNpy5amUdj/4PLnf1pduX8PajpaK5mc2mYyy9/Ag9z5/iu6B6JT7G9truG5bK5esbbTmIWOqWDlrCpcDB1X1EICI3APcBhQHBQXqssf1wMky5qdkhkbHeXjPa/nz266/qIK5ObtcMPjhMyemDCd1u4Rd6xq5YUc761rDFcqhMWYxKWdQ6ACOF513A1dMSvNnwM9E5A+BMHDDdG8kIncCdwJ0dVV+lvC9v3iJVMpZ1XPD6la2b1hZ4RxNpao8f2yY/9jbzcnBiZPMfB4Xb97ayo072mmqsUllxpiCcgaF6RrYJ/dmvg+4W1X/SkSuBP5VRHao6oSttFT1LuAugF27dp37HoslFIsnue/Rffnz267fuej6Eg73Rfjek8d5bVKfQdDn5voL2rjhgnbba8AYM61ylgzdwOqi806mNg/dAdwEoKqPi0gAaAF6y5iveXngiVeJRJ1mmPbmOq68aF2Fc1QwPJ7k+3u6efxA/4Trfq+LGy9o58YL220UkTHmrMpZQuwBNonIOuAEcDvw/klpjgHXA3eLyDYgAPSVMU/zoqr87LFCl8itb7kIl6vynbKpdIaHXulj9zMniRZtVuN2Cddta+WWnStttrExS0RGlXgqQyyVIZ7MEE9nCuepDDtX1eIt44ZdZQsKqpoSkU8AP8UZbvoNVd0nIn8O7FXV3cBngX8Skf+C07T0YdVz2YV3YR07NUB3zyAAPq+H6y7fXOEcwbH+ce7+5RGO9U9ciuLitQ38+mWdtNcHKpQzY6qbqpJIK7FUmliyULDnCvdYMneczl+LpzIkZtl7ZGtbeGkGBYDsnIN7J137QtHxy8DV5cxDKT2y90D++LIL1xLwV+7XdyKV4cfPneInz5+asJdxe72fD1y1hu0ddTO/2BhzzpJppxCPJjPEkk5BnivYo9mCv3AtTTyVmfM+4+cinspQW8Z9u6yBeY5UlUefeT1//qZLN1YsL90D4/zTQ4c5MViYb+B1C792ySretqMdj+0FbcyscoX8eCKdL+yjSadwHy8q9KPJNNFUhnQ5SvgZ+D0uAh4X/qJHoOi5nCwozNH+wz30DY4CEA76uXjr6lleUXqqyoMv9/K9p7onbG+5aUUNH7pmLSsarKnIVK9cc000mWY8mSaacAr3XGEfTebOnYI/uQCFvNclTkHudQrzgMeNP388qbD3Ovd9bqnoiEYLCnP0yNOFpqMrd65f8P0SIvEUX3/4MC8cH85f87qF37hiNW/Z1rrohsUaUyrpjDKeTDOecAr2SMIp9CcW/GnGExnSZeySdAkEvW4CHhfBbAEe8DqFedDjXM8V/k4h78bjWnr/Li0ozEE6neFXzx3Kny9009Hx/nH+189fp2+0MCO5qznE7123ztYnMktWOuP8qo8k0oWCPlEo9HPX46nM7G92HnKFfNDryhf2Ie/Egj5YdOyt8C/4hWJBYQ5eeO0EI2NO+31jXWhBZzA/ebCfux85QrKouejGHe2857KOso5AMGY+kukMY4k0kXihcB9LpPLHkeyv/lLzuIRQrqD3Oc8hr7twrei50s00i5UFhTl49JmD+eNrLtm4IHMTVJUfPXuK3c8U5vv5vS4+cu06Ll3XWPbPN2YmqYwyFk/lC/3RRIqxbOGfe06kS1vgh7xuQj6nMA/7CoV8KHsc8jmFv/1Qmj8LCrNQVfa+dCR/fs0l5W86SqUzfPPRoxNmJq9oCPAH12+w5iJTVpqdODUaTzMadwr7sUSK0Xg6f1zKX/hBr4uwL1vQ+9yEvdnnfGHvFP4u+0W/YCwozOLoyX7Gxp22/LqaIBu6Wsv6ebFEmq/8/CCvnhzNX7ugo46PXb+BoM+2vjTzF09lGImlGI3nHunCcyxVklE5bhHCfne+wM89aoqOg1437iXYEbvcWVCYxQuvncgf79i0qqxtkOPxFH/z0wMc6o3kr71pSwsfuKrL5h6YOcuoMhZPMxJLMRJPOc/54/k37QhQ43cT9nmo8TsFvfPsyV53Om2tvX5psqAwixeLgsIbNneU7XNGY0n++icHJixX8e5dHdx80Qr7x2WmSGeU0XiK4VjhMZJ9Houn5jWT1usSavwe6gKFgr7W78kX/iGf25pzljELCmeRSqXZd/BU/nzHpvIEhdFYki/9+LUJM5Tff1UXb93eVpbPM0uDqhJJpBmKOoX9UDTJULbgH42lpqxDP1dul1CXLehrA57Csd9Drd+N337lVzULCmdx8Fgf8UQSgNbGWla0lH49oWgizZd/eiAfEETgg9es4U1bytt3YRaPVEYZjiYZiqYYjCUZGk8xFHPOU+f5kz/kdVMXcFMX8DgPf/YR8BD0WqFvZmZB4SxeeK07f3zh5o6S/0NKpDJ85f6DHOlzmoxE4I43r+ONG5tL+jlmcUimM07BH00yMO4U+gPRJCOx1Hm9X9jnpj7goT7ooT5b4Ndng4ANzTTny4LCWbx0oDBH4MLNq0r63umMctdDh9h/qjDK6LevWmMBYRnIqDIcTdE/7hT+A9kgcD6Fv9/joiHgoSHopSHoFPpW8JtysqAwg3giyauHT+fPS92f8N0nj/Pc0aH8+Xsu6+DN26zJaKmJJtP0jyfpjyTpH084QWA8ec4dvXV+D40hDw0BLw0hD40BL/VBD0GvDUM2C8uCwgxePdxDOjt0r7O9kab6cMne+xev9PHAvsKOoze9YQU3X7RwS2eYc6eqjCXS9I0l6B9PciaS4EwkSaRop7u5qA94aAp5aQx6aQo5v/4bgt4luXCaWZ4sKMzgxf0T+xNK5dWTI3z78WP580vWNvDey8o31NWcu1wA6B1L0DeW4EwkQV8keU4Ls4V9bppDTsGfe1jhb5YCCwozKJ60VqqgcGY0zt8/8Hp+s46u5hB3vHmdjQSpsGjSCQC5R99YgtgcA4BbhKaQl+awl+b8sw9/mTdCMaZcLChMI5VKc+RkYd2hUqyKmkpnuOuhQ0TiTnNDfcjLJ27ciN/ajBdUOqMMjCc5PRqnJxsE5toB7HO7aAl7aQn78s8NQY9N5DLLigWFaRw/PZjvT2hvrqM2PP8dzX74zMn88hVul/AH12+gqcY37/c1ZxdPZegZjXN6NMHp0Ti9Y4k5jf33uV201nhpDftoDftoqfFS5/dYrc4sexYUpnHkRKGWsK5j/kNE93UP85PnCyOZ3nXpKja018z7fc1U44k0p0binByNc2okzsB4ctbXuARawj7aagqP+oAFAFOdLChM4/CJM/njNfMMCiPRJF//xeH8+QUdddz0hhXzek9TMJ5Ic3IkzomRGCeH4wzPoSmoxuemvdZPe62P9hofLWGfrdZpTJYFhWkc7i4EhXWdLfN6r2//6hgjUaegqgt6uOM661iej0Qqw8mRON3DMU4MxxmMzl4TaAl7WVHrZ2U2ENT47X97Y2Zi/zomUdVJzUfnHxSeOzrE3sOD+fOPvHkddUHvvPJXbTKq9I0lOD4co3soTs9o/KwLwbkE2mv8rKzzs6rOT1utD5/N/DVmziwoTNI3OMZ4LAFATchPc8P5TVobj6f41mNH8+dXbWpmR2d9SfK43EWTaY4NxTg+GOP4cOys8wNyQWBVvRME2mv9NhfAmHmwoDBJcdPR2o7m827q+f6eboaynZx1QQ+/ecXqkuRvOVJ1hokeGYxxdDBK71jirOlbwl466gN01vtZUeu3NYCMKSELCpMUdzKfb9PRa6dH+eWrhfd535Vd1ATsT10so8qpkTiHB6IcHYwyGp95uYiQ183qhgCrGwJ01PttPSBjyshKqkmOdBf1J5xHJ7Oqcs/jx/PnO9c0sGtdY0nyttSlM0r3cIxD/VGODEbP2iy0otZPV2OAroYAzSGvdc4bs0AsKExS3Mm89jyGo/7qQH9+S02vW3j/lV1VXaDlAsHr/eMcGYjNuD+wzy10NQTzgSBgtQFjKsKCQpGx8Th9g87+Bm63i462hnN6fTyV5gdPF9ZMevuFK6py1rKqcmo0zoEz4xzqn7lGEPK6WdccZF1jkJV1fpsrYMwiYEGhyJGi/oTVK5rweM7t1+rPXuxhMFLoXK62SWqD40n290U4cGZ8xiWla/1u1jeHWN8UpK3GV9W1KGMWo7IGBRG5Cfgy4Aa+pqpfnCbNbwJ/BijwvKq+v5x5OpvDE/oTzq3paCiS4L4Xipey6CDgW/5NIPFUhoNnxtnfF5lx1FDY52ZjS4gNzSFaw9Y/YMxiVragICJu4KvAjUA3sEdEdqvqy0VpNgGfB65W1UERaStXfuaieGXUcx159J/PnSKedJpJOhqDXLN5fjOhF7Nc89ArPREODUTzS4EX83tcbGwOsaklRHut1QiMWSrKWVO4HDioqocAROQe4Dbg5aI0vw98VVUHAVS1d8q7LKDJcxTmaiiS4NHXCq/99cs7cS3D9vF4KsP+vggv94wxFJ26xpBLYE1jkC2tYVY3BKyPwJglqJxBoQM4XnTeDVwxKc1mABF5DKeJ6c9U9b7JbyQidwJ3AnR1dZUls5lMhu6ewpIU5xIUfvZSD6m082t5bWuIHZ11Jc9fJfVHErx0eozXzoxPWytoCXvZ2hZmY3PIRg0Zs8SVMyhM9zNxconiATYB1wGdwCMiskNVhya8SPUu4C6AXbt2neOW6HNzZiiS30OhvjZIOOif0+vGYil+8Wpf/vydO1cti6YSVeXoYIwXTo1yciQ+5b7XLWxsCbG9rYbWKhxhZcxyVc6g0A0Ur+3QCZycJs0TqpoEDovIfpwgsaeM+ZpWb/9I/ritqXbOr/v5vp4JfQkXdS3t9Y1SGeW1vgjPnxyddhnqppCXHStq2NgSsoXNQjG+AAAb+UlEQVTmjFmGyhkU9gCbRGQdcAK4HZg8sugHwPuAu0WkBac56VAZ8zSjnuKg0Dy35p9oIs2D+wrdILfsXLlkawmJVIZ9PWO8cGqUaHLivAIB1jcH2bGilhXWaWzMsla2oKCqKRH5BPBTnP6Cb6jqPhH5c2Cvqu7O3nubiLwMpIE/UtX+md+1fHr7R/PHK+YYFB5+pZfx7Hj8tjr/klzOIp7K8MKpUV48NTZltrHPLWxrr2HHihpqbQ8CY6pCWf+lq+q9wL2Trn2h6FiBz2QfFdVTFBTaW2ZvPspklIdeKfQlvOOiFUtqxFEhGIySSE/spgn73LxhZS3b2sL4PNZEZEw1sZ9/Wb0DhaDQ1jR7TeHF7mEGspO1wn43V2yY/17OCyGZzvDiqTGeOzk6pWZQH/Cws6OWzS1hG05qTJWyoJDVc6a4T2H2msIvimoJV29uWfS/qNMZ5dXeCE93jzCenLgERUPQw67OetY3B3FZf4ExVc2CApBIphgadVY2dYnQ0lBz1vRnRuO82D2cP79uW2tZ8zcfqsqxoRiPHx2aMuGsPuBh1+o6NjSHLBgYYwALCsDE/oSWxhrcswy1fGT/GTTbDL+9o462ukA5s3feBseTPHZkkO7hifMMwj43uzrr2NIWtmBgjJnAggKT5ijM0nSUSmd4ZH+h6Wgx1hIS6QxPHx/hxdOjFE9A9rqFSzrquHBlre1jbIyZlgUFJs1RmKWT+dmjQ4xkm2EaQl4u6jq3PRfKSVU51B/lsSNDU/oNtreHuWx1vW1laYw5KwsKTJyj0N5y9qDwyP7Cwndv2tKyaEbpjMVTPHJ4kKODsQnXV9b6uWZdA81hW4rCGDO7swYFEblbVT+cPf6Qqn5zQXK1wIqHo55t4tpINMkrJwu1imu2VH55bFXl5Z4ITxwdIlnUVhTyunnjmno2tYRsBrIxZs5mqylcVHT8KWBZBoXijuaz9Sk8fXgw38G8sb2G5pq5LZpXLmPxFA+/Pkj38MTawfb2MFd0NeBf5MNkjTGLz2xBoSwrki4mqjpp3aOZg8KeQwP548vWV3ZJi9f6Ijx6eGjCBLT6gIfrNjSxsq6ywcoYs3TNFhQ6ReRvcdZEyx3nqeony5azBTI2Hicac2Ym+7we6muC06YbiiQ40DMGgAjsWte0YHkslkhnePTwIK/1jU+4ftHKWi7rqrdRRcaYeZktKPxR0fHecmakUiZ0MjfXztj+vreo6WjLylrqQ96FyN4EfWMJ7j/Qz0jRktZ1fg9v2Wi1A2NMaZw1KCzXjuVip+c4HHVC01EFagmv9IzxyOHBCfMOtrSGuWZdA17b18AYUyKzliYi8iEReUZEItnHXhH54EJkbiEUT1ybaXXUM6NxXu+NAM4+xJesW7i5CamM8vDrA/ziUCEgeN3CWzc28ZaNTRYQjDElNduQ1A8Cn8ZZ2voZnL6FS4D/KSKo6r+UP4vlNZfVUfceLuzdvK2jjtrAwjQdRRJp7nv1DH2RRP5aU8jL27c0U79AeTDGVJfZ+hT+AHi3qh4puvagiLwXuAdY+kFhDhPXni4KCpevX5imo76xBPftP0MkUZiZvKklxLXrG612YIwpm9mCQt2kgACAqh4RkbltT7bIFQ9HbZ9mOOpINMmRM4Wmo51ryt90dHggygMH+kll24sEuGptAztW1NhENGNMWc0WFKLneW9JUNVJzUdTg8IrJ0byo47Wt9UQLvO2lPtOOx3KOT63i7dtbqazYXGuxGqMWV5mK+G2icgL01wXYH0Z8rOghseipLOTv8JBP8HA1PWBivdNuKCzfJUjVWVv9whPdxdqLnUBD+/Y2kJj0PoPjDELYy7LXLQDxyddXwOcLEuOFtDwaKGy01gXmnJfVXn5RKGQ3tFZX5Z8qCqPHB7k5Z5I/lpbjY93bG2xVU2NMQtqth7LvwZGVPVo8QMYz95b0oqDQl3N1OaZ4wPR/DLZNQEPa1umBo75yqjy8OsTA8LqhgC/tr3VAoIxZsHNVlNYq6pTmo9Uda+IrC1LjhbQyFhhIbm6aZa3eKmo6Wj7qrqSd/I6AWFgwpIVG5tDvGVj06JZktsYU11mCwpn692cfpGgJWR4rFBTmG7No5eOF4LCjtWl7U/IqPLQwQEOnCkEhC2tYd68odG2yDTGVMxszUd7ROT3J18UkTuAp8uTpYVT3HxUXzsxKEQT6fwsZoALOkrXn5DrQygOCFvbwlxnAcEYU2Gz1RQ+Dfx/IvIBCkFgF+AD3l3OjC2Es9UU9p8aJZ2dJ9DVHCrpAnhPHR/mlaI+hG3tYa5d12hzEIwxFTfbgng9wFUi8hZgR/byj1X1wbLnbAGcraZQrqGoz58c4dkThbkRm1pCFhCMMYvGnGZiqepDwENlzsuCm1BTmBQUXu4u/VDUA2ciPH60EGzWNAa4bkOTBQRjzKJR1YvojMwQFIYiCfpG44CzIumGtvC8P+v0SJyHDhaW315Z5+fGzS02ysgYs6hUdVAYLh6SGi4MtCruYF7bGsYzzwXoRmIpfvramfzS141BDzdtabFd0owxi07VBoVEMpXfhtPlclETKuxc9nrvWP54Y3vNvD4nnsrwk1f7iCad5TQCHhfv2NqK31O1f3pjzCJW1pJJRG4Skf0iclBEPneWdL8uIioiu8qZn2LFE9fqawIT2vUPFdUUNrSdf1DQ7OS0weysaJfATVtaqAuUd1E9Y4w5X2ULCiLiBr4KvAPYDrxPRLZPk64W+CTwZLnyMp2JS1wU+hOS6Ux+qWyA9fPoT3jx1BiHBwqfc92GJlbYXsrGmEWsnDWFy4GDqnpIVRM4m/LcNk26/wP4SyA2zb2ymWmOwvH+cVJpp/G/rc5P3XmuUHp6JM4Tx4by5ztW1LC5df4d1sYYU07lDAodTFxdtTt7LU9ELgZWq+p/ljEf05ppjsLBnkJ/wvnWEqLJNPcf6M93LLfV+LhyATbnMcaY+SpnUJhuaI3mb4q4cFZa/eysbyRyp4jsFZG9fX19JcncTDWF4pFH59PJnOtHyG2j6fe4uHFzsw09NcYsCeUMCt3A6qLzTibuwVCLM0v6YRE5ArwR2D1dZ7Oq3qWqu1R1V2tra0kyN90cBVXl9Z75jTx6tS/C0cFCS9j1G5uoLfNubcYYUyrlDAp7gE0isk5EfMDtwO7cTVUdVtUWVV2rqmuBJ4BbVXVvGfOUVzxHob7WmaPQP5ZgaDwJQMDrYlXDuS0EOxJL8avDE/sRuhqX/GKyxpgqUragoKop4BPAT4FXgO+q6j4R+XMRubVcnztXw6OFFUpzo4+Kh6Kub6vBdQ5NPhlVHnp9gGS2I6E+4OGKrvLs1GaMMeVS1nYNVb0XuHfStS/MkPa6cuZlsgk1hWxQKJ60dq6dzC+dGuPUiLM0hgBv3diEd54zoY0xZqFVbalVXFPI9SkUB4VzmbQ2Fk+xp2hDnks662ivtfkIxpilpyqDgqpOqSkk0xmO9xc6n8+lpvDYkaF8s1Fj0MMlHaXdpc0YYxZKVQaFaCxJKuUMGfV5PQT8Xk4PxfKb6rTU+gjPccTQ0cHohFnLb1rfaMNPjTFLVlUGhenmKHQXFeydTaE5vU8yneHRw4P58y2tYVbVnW1ba2OMWdyqMigUz1Goq3EK8RODRUFhjsNInzs5ymi8MEntjWtstJExZmmryqBQ3J/QUOvUCroHCh3PHU2zB4VIIs1zJwvbal7RVU/Q6y5hLo0xZuFVZ1AonqNQO7Wm0DGHmsKe48OFPoiwl20l2J3NGGMqrTqDQnFNoSZIJJ5iMOLMZPa4hfb6s/cLDI4nebVootsbuxpsn2VjzLJQnUFhQk0hOKGTeVVDcNbRQ08eK8xJ6Kz309lgncvGmOWhOoPCpDkKxU1HqxrPXsCfGolzpCj9G21JbGPMMlKVQWHyCqnFncyzDUd9qqiWsKklREvYV/oMGmNMhVRlUJiwwU5NkJNFS113nmXk0amROKdGnfWNXAKX24J3xphlpjqDQlFNoTbsn/MchWdOjOSPN7WEbZ8EY8yyU3VBQVUZKaoppMVNNLtLWtjvpj40/Z7MZyIJjg8VahQXd9SWN6PGGFMBVRcURiOx/J6goYCP08OJ/L2OxuCMQ0ufLaolbGgO0hCcPngYY8xSVn1BYTyeP66rCdA9OPuaR0PRJK8XraB6sa2CaoxZpqouKESjhZpBMODjxMDsM5mLl7NY3RCwEUfGmGWr+oJCPJk/Dvq9dA8WD0edGhRiyTQH+gppbK8EY8xyVtVBIeD30jNcaE6arqawvy9CWgtrHK2otVqCMWb5qr6gECs0H2U83vyido1hLwHfxFVOVZV9PYU1ji5YUWNrHBljlrUqDAqFmkJaCkGgdZo9lU8MxxmJpQDwuV1sbJ7b5jvGGLNUVV1QGC+qKaQoTD5rm2bHtH09Y/njza0hvO6q+3MZY6pM1ZVysaI+hbgWmoJa6ybWFMbiKY4UjUy6oL2m/JkzxpgKq7qgEI0XagrxdFFQmNR89EpvJD/JbVWdn8YZZjobY8xyUnVBYbyoT2E8VbjeUjSqSFUnbKJjtQRjTLWouhXdYtk+BQXGkkpNNha0FNUUTo3GiWTXQwp4XKydw57NxpiFk0wm6e7uJhaLzZ64ygQCATo7O/F6z691o+qCQq6mkEFIZ9uH/F4XtYHCn+LAmcJktQ3NoVl3YjPGLKzu7m5qa2tZu3atDRMvoqr09/fT3d3NunXrzus9qq75KNenkMSdL+xba/35/7HSGeVQ0TpHG1tsGKoxi00sFqO5udkCwiQiQnNz87xqUNUXFLI1hRRu3C7n6xePPOoejhFPZQAI+9w2g9mYRcoCwvTm+3epvqCQqymIG1d23kHxyKODRU1HG1tC9j+eMaaqVF9QyNYUkrgKNYVsUEimMxPmJmyypiNjTJUpa1AQkZtEZL+IHBSRz01z/zMi8rKIvCAiD4jImnLmR1Xzax+livoUciOPjg7GSGbXQmoIemi2uQnGmCpTtqAgIm7gq8A7gO3A+0Rk+6RkzwK7VPUNwPeBvyxXfgDiiVR+Qlra5ck3DeVqCtZ0ZIxZaMePH+ctb3kL27Zt44ILLuDLX/5yRfNTzprC5cBBVT2kqgngHuC24gSq+pCq5kriJ4DOMuYnv2y2AupyhqCKQHOtj2Q6w7GholFHtvidMWYBeDwe/uqv/opXXnmFJ554gq9+9au8/PLLlctPGd+7AzhedN4NXHGW9HcAP5nuhojcCdwJ0NXVdd4ZGi9qOsp1MjeEvHjdLo4MRMm2HNEU8toezMYsEe/91D+U7b3//csfO+v9ffv28alPfYpjx47xO7/zO/T29vLBD36Qyy67bM6fsXLlSlauXAlAbW0t27Zt48SJE2zfPrlhZWGUMyhM1/ai01xDRH4b2AW8ebr7qnoXcBfArl27pn2PuYidpZP5aFEtoath6oqpxhhTLBaL8Ru/8Rt873vfY/369WzdupVLL710QkB405vexOjo6JTXfulLX+KGG26Ycv3IkSM8++yzXHHF2X4/l1c5g0I3sLrovBM4OTmRiNwA/Hfgzaoan3y/lHI1heKJa211AVSVY4OFyR5rZtir2Rhjcn7+859z8cUXc8EFFwCQSCT47Gc/OyHNI488Muf3Gxsb473vfS9/8zd/Q11d5bb9LWdQ2ANsEpF1wAngduD9xQlE5GLgH4GbVLW3jHkBCn0KKXHjKpq4NjCezK915HO7aLcJa8YsGbM18ZTLs88+yyWXXALAyZMnqamp4eqrr56QZq41hWQyyXvf+14+8IEP8J73vKe8GZ9F2YKCqqZE5BPATwE38A1V3Scifw7sVdXdwP8EaoDvZUf6HFPVW8uVp1jREhd+d2Hk0bGhQi1hdYMfl406MsbMwu/3093dDcDnP/95EonElDRzqSmoKnfccQfbtm3jM5/5TMnzea7KOk9BVe9V1c2qukFV/0f22heyAQFVvUFV21V1Z/ZRtoAAMB6dusRFS61vQtNRlzUdGWPm4P3vfz+//OUv2bJlCxdddBFXXnkln/70p8/5fR577DH+9V//lQcffJCdO3eyc+dO7r333jLkeG6qapXUaDyBMrGjuTbo5fRooSvDOpmNMXPR2dnJ008/Pe/3ueaaa1A97/EzJVdVy1xEY0kyCBlx4XIJfq+L4XhhQltr2EfQ665oHo0xppKqKyjEE6SyX9ntdtFc4+P4UFEtodFqCcaY6lZVQWE8liSdCwouoT7onTCL2YaiGmOqXVUFhVgsQQqnecjlchEMeIgmnb0TAh4XrWGbxWyMqW5VFRSi8SRpydUUXLg8ha+/ss5vC+AZY6peVQWF8Vii0HzkFrQoCKws2mjHGGOqVVUFhVg8WehodrmIpwvDwFbUWVAwxpiqCgoTagoeN6lsTPC4xDbUMcYYqiwoRItGH3l8brzZpS7aa335BfKMMWahfeQjH6GtrY0dO3ZUOivVExRUlWg8SSrb0ezxuvFk91RYYf0JxpgK+vCHP8x9991X6WwAVbTMRSKZIq2gIog4QSFbUbBOZmOWsN/72t6yvffXfm/XWe+XYpMdgGuvvZYjR47MI6elUzVBIVrUyez1uPF63SCCgC2VbYw5Z+XYZGcxqJqgMB4tdDKHwwG82aajlrAvf2yMMXNV6k12FouqCQqxeKGTOVwTwJPtWF5ZZ7UEY5ay2Zp4yqWUm+wsJlUTFMZjhcXwgsFC7cA6mY0x56NUm+wsNlXTbpJb4kIEAkEfnmwvswUFY8z5KNUmOwDve9/7uPLKK9m/fz+dnZ18/etfL3Fu565qagqx7BwFv9+HSwSvW6gLeAj5bP8EY8y5K9UmOwDf+c53SvI+pVA1NYVc81Eg6EVE8LhtVVRjjJmsaoJCNNvRHAh4EQGv20VzyDqZjTGmWPUEhey6R36/Ewg8bqHZagrGGDNB1QSFSCxBWnI1BbFF8IwxZhpVExQGxhJ4vW7cbhcetxDwuglbJ7MxxkxQNUFheDxJIJBtOsrWEmynNWOMmahqgsJoLIU/4DQXed0uWqw/wRhjpqiaoDASTeVrCjbyyBhjplc1QWEskSaQrSn4PC4beWSMWTTuu+8+tmzZwsaNG/niF784Y7q1a9dy4YUXsnPnTnbtKs+aT1UzozmaylDvc76uz+OiMWhBwRhTeel0mo9//OPcf//9dHZ2ctlll3Hrrbeyffv2adM/9NBDtLS0lC0/VRMUUq5CEGgIem37TWOWiX94/HjZ3vtjV64+6/1SbLLz1FNPsXHjRtavXw/A7bffzg9/+MMZg0K5VUVQUFXwFoJCuy2CZ4yZp1JtsnPixAlWry4En87OTp588slpP1NEeNvb3oaI8NGPfpQ777yzxN+qSoJCMpXG4y8Ego7GYAVzY4xZDkq1yY6qTrk203D5xx57jFWrVtHb28uNN97I1q1bufbaa88j9zMra1AQkZuALwNu4Guq+sVJ9/3AvwCXAv3Ab6nqkVLnYzyawBf05T6T1Q2BUn+EMaZCZmviKZdSbbLT2dnJ8eOFJrDu7m5WrVo17Wfmrre1tfHud7+bp556aukEBRFxA18FbgS6gT0isltVXy5KdgcwqKobReR24P8GfqvUeekdGs+veeQSWFFnzUfGmPkp1SY7l112GQcOHODw4cN0dHRwzz338O1vf3tKukgkQiaToba2lkgkws9+9jO+8IUvzP+LTFLOIamXAwdV9ZCqJoB7gNsmpbkN+Gb2+PvA9VKGacbHB6K4sh3Lmk4T9NryFsaY+SnVJjsej4evfOUrvP3tb2fbtm385m/+Zr5JCuDmm2/m5MmT9PT0cM0113DRRRdx+eWXc8stt3DTTTeV8is5+Sn5OxZ0AMXDArqBK2ZKo6opERkGmoEzxYlE5E7gToCurq5zzsjx4WjhJJU+59cbY8xkpdxk5+abb+bmm2+e9t69996bP37++edL8nlnU86gMN0v/sk9KnNJg6reBdwFsGvXrqm9MrO4amMLA8NRBmMpmkPWyWyMMTMpZ1DoBop7gDqBkzOk6RYRD1APDJQ6I5s7mvhMR1Op39YYY5adcvYp7AE2icg6EfEBtwO7J6XZDXwoe/zrwIM63fgsY4yZxIqK6c3371K2oKCqKeATwE+BV4Dvquo+EflzEbk1m+zrQLOIHAQ+A3yuXPkxxiwfgUCA/v5+CwyTqCr9/f0EAuc/7F6W2h91165dunfv3kpnwxhTQclkku7ubmKxWKWzsugEAgE6Ozvxeieu7yYiT6vqrKvoVcWMZmPM8uL1elm3bl2ls7EsVc3S2cYYY2ZnQcEYY0yeBQVjjDF5S66jWUT6gKOVzkeFtDBptneVqfbvD/Y3sO9//t9/jaq2zpZoyQWFaiYie+cyemC5qvbvD/Y3sO9f/u9vzUfGGGPyLCgYY4zJs6CwtNxV6QxUWLV/f7C/gX3/MrM+BWOMMXlWUzDGGJNnQcEYY0yeBYUlQERWi8hDIvKKiOwTkU9VOk+VICJuEXlWRP6z0nlZaCLSICLfF5FXs/8fXFnpPC0kEfkv2f/3XxKR74jI+S8DugSIyDdEpFdEXiq61iQi94vIgexzYzk+24LC0pACPquq24A3Ah8Xke0VzlMlfApnGfZq9GXgPlXdClxEFf0dRKQD+CSwS1V3AG6c/VmWs7uByRswfw54QFU3AQ9Qpq0GLCgsAap6SlWfyR6P4hQIHZXN1cISkU7gFuBrlc7LQhOROuBanP1HUNWEqg5VNlcLzgMEszs0hpi6i+Oyoqq/ZOoulLcB38wefxN4Vzk+24LCEiMia4GLgScrm5MF9zfAHwOZSmekAtYDfcA/Z5vPviYi4UpnaqGo6gngS8Ax4BQwrKo/q2yuKqJdVU+B80MRaCvHh1hQWEJEpAb4d+DTqjpS6fwsFBF5J9Crqk9XOi8V4gEuAf5eVS8GIlTRLoXZtvPbgHXAKiAsIr9d2VwtXxYUlggR8eIEhH9T1f+odH4W2NXArSJyBLgHeKuIfKuyWVpQ3UC3quZqh9/HCRLV4gbgsKr2qWoS+A/gqgrnqRJ6RGQlQPa5txwfYkFhCRARwWlPfkVV/59K52ehqernVbVTVdfidDA+qKpV80tRVU8Dx0VkS/bS9cDLFczSQjsGvFFEQtl/C9dTRR3tRXYDH8oefwj4YTk+xLbjXBquBn4HeFFEnste+2+qem8F82QW1h8C/yYiPuAQ8LsVzs+CUdUnReT7wDM4I/GeZZkvdyEi3wGuA1pEpBv4U+CLwHdF5A6cQPkbZflsW+bCGGNMjjUfGWOMybOgYIwxJs+CgjHGmDwLCsYYY/IsKBhjjMmzoGCMMSbPgoIxxpg8CwrGzJOIXCYiL4hIQETC2XX/d1Q6X8acD5u8ZkwJiMj/CQSAIM46RX9R4SwZc14sKBhTAtnlJ/YAMeAqVU1XOEvGnBdrPjKmNJqAGqAWp8ZgzJJkNQVjSkBEduMs670OWKmqn6hwlow5L7ZKqjHzJCIfBFKq+m0RcQO/EpG3quqDlc6bMefKagrGGGPyrE/BGGNMngUFY4wxeRYUjDHG5FlQMMYYk2dBwRhjTJ4FBWOMMXkWFIwxxuT9/4wXWVV+FlMgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin = 0.5\n",
    "\n",
    "thinkplot.PrePlot(3)\n",
    "for alpha in [2.0, 1.0, 0.5]:\n",
    "    xs, ps = thinkstats2.RenderParetoCdf(xmin, alpha, 0, 10.0, n=100) \n",
    "    thinkplot.Plot(xs, ps, label=r'$\\alpha=%g$' % alpha)\n",
    "    \n",
    "thinkplot.Config(title='Pareto CDF', xlabel='x',\n",
    "                 ylabel='CDF', loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of populations for cities and towns is sometimes said to be Pareto-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cities/towns 19515\n"
     ]
    }
   ],
   "source": [
    "import populations\n",
    "\n",
    "pops = populations.ReadData()\n",
    "print('Number of cities/towns', len(pops))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of population for cities and towns in the U.S., along with a Pareto model.  The model fits the data well in the tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_pops = np.log10(pops)\n",
    "cdf = thinkstats2.Cdf(pops, label='data')\n",
    "cdf_log = thinkstats2.Cdf(log_pops, label='data')\n",
    "\n",
    "# pareto plot\n",
    "xs, ys = thinkstats2.RenderParetoCdf(xmin=5000, alpha=1.4, low=0, high=1e7)\n",
    "thinkplot.Plot(np.log10(xs), 1-ys, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf_log, complement=True) \n",
    "thinkplot.Config(xlabel='log10 population',\n",
    "                 ylabel='CCDF',\n",
    "                 yscale='log', loc='lower left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lognormal model might be a better fit for this data (as is often the case for things that are supposed to be Pareto)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(cols=2)\n",
    "\n",
    "mu, sigma = log_pops.mean(), log_pops.std()\n",
    "xs, ps = thinkstats2.RenderNormalCdf(mu, sigma, low=0, high=8)\n",
    "thinkplot.Plot(xs, ps, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf_log) \n",
    "thinkplot.Config(xlabel='log10 population',\n",
    "                 ylabel='CDF', loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a normal probability plot for the log-populations.  The model fits the data well except in the right tail, where the biggest cities are bigger than expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkstats2.NormalProbabilityPlot(log_pops, label='data')\n",
    "thinkplot.Config(xlabel='Random variate',\n",
    "                 ylabel='log10 population',\n",
    "                 xlim=[-5, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random variates\n",
    "\n",
    "When we have an analytic CDF, we can sometimes invert it to generate random values.  The following function generates values from an exponential distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def expovariate(lam):\n",
    "    p = random.random()\n",
    "    x = -np.log(1-p) / lam\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test it by generating a sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = [expovariate(lam=2) for _ in range(1000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plotting the CCDF on a log-y scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf = thinkstats2.Cdf(t)\n",
    "\n",
    "thinkplot.Cdf(cdf, complement=True)\n",
    "thinkplot.Config(xlabel='Exponential variate', ylabel='CCDF', yscale='log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A straight line is consistent with an exponential distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As an exercise, write a function that generates a Pareto variate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** In the BRFSS (see Section 5.4), the distribution of heights is roughly normal with parameters µ = 178 cm and σ = 7.7 cm for men, and µ = 163 cm and σ = 7.3 cm for women.\n",
    "\n",
    "In order to join Blue Man Group, you have to be male between 5’10” and 6’1” (see http://bluemancasting.com). What percentage of the U.S. male population is in this range? Hint: use `scipy.stats.norm.cdf`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`scipy.stats` contains objects that represent analytic distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example <tt>scipy.stats.norm</tt> represents a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.stats._distn_infrastructure.rv_frozen"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu = 178\n",
    "sigma = 7.7\n",
    "dist = scipy.stats.norm(loc=mu, scale=sigma)\n",
    "type(dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"frozen random variable\" can compute its mean and standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178.0, 7.7)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist.mean(), dist.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can also evaluate its CDF.  How many people are more than one standard deviation below the mean?  About 16%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1586552539314574"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist.cdf(mu-sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many people are between 5'10\" and 6'1\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.48963902786483265, 0.8317337108107857, 0.3420946829459531)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "low = dist.cdf(177.8)    # 5'10\"\n",
    "high = dist.cdf(185.4)   # 6'1\"\n",
    "low, high, high-low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** To get a feel for the Pareto distribution, let’s see how different the world would be if the distribution of human height were Pareto. With the parameters xm = 1 m and α = 1.7, we get a distribution with a reasonable minimum, 1 m, and median, 1.5 m.\n",
    "\n",
    "Plot this distribution. What is the mean human height in Pareto world? What fraction of the population is shorter than the mean? If there are 7 billion people in Pareto world, how many do we expect to be taller than 1 km? How tall do we expect the tallest person to be?\n",
    "\n",
    "`scipy.stats.pareto` represents a pareto distribution.  In Pareto world, the distribution of human heights has parameters alpha=1.7 and xmin=1 meter.  So the shortest person is 100 cm and the median is 150."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5034066538560549"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = 1.7\n",
    "xmin = 1       # meter\n",
    "dist = scipy.stats.pareto(b=alpha, scale=xmin)\n",
    "dist.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the mean height in Pareto world?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.428571428571429"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "dist.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What fraction of people are shorter than the mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.778739697565288"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "dist.cdf(dist.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of 7 billion people, how many do we expect to be taller than 1 km?  You could use <tt>dist.cdf</tt> or <tt>dist.sf</tt>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55602.976430479954, 55602.97643069972)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "(1 - dist.cdf(1000)) * 7e9, dist.sf(1000) * 7e9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How tall do we expect the tallest person to be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0525455861201714"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# One way to solve this is to search for a height that we\n",
    "# expect one person out of 7 billion to exceed.\n",
    "\n",
    "# It comes in at roughly 600 kilometers.\n",
    "\n",
    "dist.sf(600000) * 7e9            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "618349.6106759505"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# Another way is to use `ppf`, which evaluates the \"percent point function\", which\n",
    "# is the inverse CDF.  So we can compute the height in meters that corresponds to\n",
    "# the probability (1 - 1/7e9).\n",
    "\n",
    "dist.ppf(1 - 1/7e9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The Weibull distribution is a generalization of the exponential distribution that comes up in failure analysis (see http://wikipedia.org/wiki/Weibull_distribution). Its CDF is\n",
    "\n",
    "$\\mathrm{CDF}(x) = 1 − \\exp[−(x / λ)^k]$ \n",
    "\n",
    "Can you find a transformation that makes a Weibull distribution look like a straight line? What do the slope and intercept of the line indicate?\n",
    "\n",
    "Use `random.weibullvariate` to generate a sample from a Weibull distribution and use it to test your transformation.\n",
    "\n",
    "Generate a sample from a Weibull distribution and plot it using a transform that makes a Weibull distribution look like a straight line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are stuck, you can get a hint from `thinkplot.Cdf`, which provides a transform that makes the CDF of a Weibull distribution look like a straight line.  Here's an example that shows how it's used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = [random.weibullvariate(2, 1) for _ in range(1000)]\n",
    "cdf = thinkstats2.Cdf(sample)\n",
    "thinkplot.Cdf(cdf, transform='weibull')\n",
    "thinkplot.Config(xlabel='Weibull variate', ylabel='CCDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** For small values of `n`, we don’t expect an empirical distribution to fit an analytic distribution exactly. One way to evaluate the quality of fit is to generate a sample from an analytic distribution and see how well it matches the data.\n",
    "\n",
    "For example, in Section 5.1 we plotted the distribution of time between births and saw that it is approximately exponential. But the distribution is based on only 44 data points. To see whether the data might have come from an exponential distribution, generate 44 values from an exponential distribution with the same mean as the data, about 33 minutes between births.\n",
    "\n",
    "Plot the distribution of the random values and compare it to the actual distribution. You can use random.expovariate to generate the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32.72727272727273, 30.514962897557755)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import analytic\n",
    "\n",
    "df = analytic.ReadBabyBoom()\n",
    "diffs = df.minutes.diff()\n",
    "cdf = thinkstats2.Cdf(diffs, label='actual')\n",
    "\n",
    "n = len(diffs)\n",
    "lam = 44.0 / 24 / 60\n",
    "sample = [random.expovariate(lam) for _ in range(n)]\n",
    "\n",
    "1/lam, np.mean(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "model = thinkstats2.Cdf(sample, label='model')\n",
    "    \n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Cdfs([cdf, model], complement=True)\n",
    "thinkplot.Config(xlabel='Time between births (minutes)',\n",
    "                ylabel='CCDF',\n",
    "                yscale='log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# If you plot distributions for a large number of samples, you get a sense\n",
    "# of how much random variation to expect.  In this case, the data fall within\n",
    "# the range we expect, so there is no compelling reason to think it is\n",
    "# not exponential.\n",
    "\n",
    "for i in range(100):\n",
    "    sample = [random.expovariate(lam) for _ in range(n)]\n",
    "    thinkplot.Cdf(thinkstats2.Cdf(sample), complement=True, color='0.9')\n",
    "    \n",
    "thinkplot.Cdf(cdf, complement=True)\n",
    "thinkplot.Config(xlabel='Time between births (minutes)',\n",
    "                ylabel='CCDF',\n",
    "                yscale='log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Worked Example:** The distributions of wealth and income are sometimes modeled using lognormal and Pareto distributions. To see which is better, let’s look at some data.\n",
    "\n",
    "The Current Population Survey (CPS) is a joint effort of the Bureau of Labor Statistics and the Census Bureau to study income and related variables. Data collected in 2013 is available from http://www.census.gov/hhes/www/cpstables/032013/hhinc/toc.htm. I downloaded `hinc06.xls`, which is an Excel spreadsheet with information about household income, and converted it to `hinc06.csv`, a CSV file you will find in the repository for this book. You will also find `hinc.py`, which reads this file.\n",
    "\n",
    "Extract the distribution of incomes from this dataset. Are any of the analytic distributions in this chapter a good model of the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>freq</th>\n",
       "      <th>cumsum</th>\n",
       "      <th>ps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.999000e+03</td>\n",
       "      <td>4204</td>\n",
       "      <td>4204</td>\n",
       "      <td>0.034330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.999000e+03</td>\n",
       "      <td>4729</td>\n",
       "      <td>8933</td>\n",
       "      <td>0.072947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.499900e+04</td>\n",
       "      <td>6982</td>\n",
       "      <td>15915</td>\n",
       "      <td>0.129963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.999900e+04</td>\n",
       "      <td>7157</td>\n",
       "      <td>23072</td>\n",
       "      <td>0.188407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.499900e+04</td>\n",
       "      <td>7131</td>\n",
       "      <td>30203</td>\n",
       "      <td>0.246640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.999900e+04</td>\n",
       "      <td>6740</td>\n",
       "      <td>36943</td>\n",
       "      <td>0.301679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.499900e+04</td>\n",
       "      <td>6354</td>\n",
       "      <td>43297</td>\n",
       "      <td>0.353566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.999900e+04</td>\n",
       "      <td>5832</td>\n",
       "      <td>49129</td>\n",
       "      <td>0.401191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.499900e+04</td>\n",
       "      <td>5547</td>\n",
       "      <td>54676</td>\n",
       "      <td>0.446488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.999900e+04</td>\n",
       "      <td>5254</td>\n",
       "      <td>59930</td>\n",
       "      <td>0.489392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.499900e+04</td>\n",
       "      <td>5102</td>\n",
       "      <td>65032</td>\n",
       "      <td>0.531056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.999900e+04</td>\n",
       "      <td>4256</td>\n",
       "      <td>69288</td>\n",
       "      <td>0.565810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.499900e+04</td>\n",
       "      <td>4356</td>\n",
       "      <td>73644</td>\n",
       "      <td>0.601382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.999900e+04</td>\n",
       "      <td>3949</td>\n",
       "      <td>77593</td>\n",
       "      <td>0.633629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.499900e+04</td>\n",
       "      <td>3756</td>\n",
       "      <td>81349</td>\n",
       "      <td>0.664301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.999900e+04</td>\n",
       "      <td>3414</td>\n",
       "      <td>84763</td>\n",
       "      <td>0.692180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.499900e+04</td>\n",
       "      <td>3326</td>\n",
       "      <td>88089</td>\n",
       "      <td>0.719341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.999900e+04</td>\n",
       "      <td>2643</td>\n",
       "      <td>90732</td>\n",
       "      <td>0.740923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.499900e+04</td>\n",
       "      <td>2678</td>\n",
       "      <td>93410</td>\n",
       "      <td>0.762792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9.999900e+04</td>\n",
       "      <td>2223</td>\n",
       "      <td>95633</td>\n",
       "      <td>0.780945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.049990e+05</td>\n",
       "      <td>2606</td>\n",
       "      <td>98239</td>\n",
       "      <td>0.802226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.099990e+05</td>\n",
       "      <td>1838</td>\n",
       "      <td>100077</td>\n",
       "      <td>0.817235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.149990e+05</td>\n",
       "      <td>1986</td>\n",
       "      <td>102063</td>\n",
       "      <td>0.833453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.199990e+05</td>\n",
       "      <td>1464</td>\n",
       "      <td>103527</td>\n",
       "      <td>0.845408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.249990e+05</td>\n",
       "      <td>1596</td>\n",
       "      <td>105123</td>\n",
       "      <td>0.858441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.299990e+05</td>\n",
       "      <td>1327</td>\n",
       "      <td>106450</td>\n",
       "      <td>0.869278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.349990e+05</td>\n",
       "      <td>1253</td>\n",
       "      <td>107703</td>\n",
       "      <td>0.879510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.399990e+05</td>\n",
       "      <td>1140</td>\n",
       "      <td>108843</td>\n",
       "      <td>0.888819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.449990e+05</td>\n",
       "      <td>1119</td>\n",
       "      <td>109962</td>\n",
       "      <td>0.897957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.499990e+05</td>\n",
       "      <td>920</td>\n",
       "      <td>110882</td>\n",
       "      <td>0.905470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.549990e+05</td>\n",
       "      <td>1143</td>\n",
       "      <td>112025</td>\n",
       "      <td>0.914803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1.599990e+05</td>\n",
       "      <td>805</td>\n",
       "      <td>112830</td>\n",
       "      <td>0.921377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.649990e+05</td>\n",
       "      <td>731</td>\n",
       "      <td>113561</td>\n",
       "      <td>0.927347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.699990e+05</td>\n",
       "      <td>575</td>\n",
       "      <td>114136</td>\n",
       "      <td>0.932042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.749990e+05</td>\n",
       "      <td>616</td>\n",
       "      <td>114752</td>\n",
       "      <td>0.937072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.799990e+05</td>\n",
       "      <td>570</td>\n",
       "      <td>115322</td>\n",
       "      <td>0.941727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.849990e+05</td>\n",
       "      <td>502</td>\n",
       "      <td>115824</td>\n",
       "      <td>0.945826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.899990e+05</td>\n",
       "      <td>364</td>\n",
       "      <td>116188</td>\n",
       "      <td>0.948799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1.949990e+05</td>\n",
       "      <td>432</td>\n",
       "      <td>116620</td>\n",
       "      <td>0.952327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.999990e+05</td>\n",
       "      <td>378</td>\n",
       "      <td>116998</td>\n",
       "      <td>0.955413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.499990e+05</td>\n",
       "      <td>2549</td>\n",
       "      <td>119547</td>\n",
       "      <td>0.976229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>inf</td>\n",
       "      <td>2911</td>\n",
       "      <td>122458</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          income  freq  cumsum        ps\n",
       "0   4.999000e+03  4204    4204  0.034330\n",
       "1   9.999000e+03  4729    8933  0.072947\n",
       "2   1.499900e+04  6982   15915  0.129963\n",
       "3   1.999900e+04  7157   23072  0.188407\n",
       "4   2.499900e+04  7131   30203  0.246640\n",
       "5   2.999900e+04  6740   36943  0.301679\n",
       "6   3.499900e+04  6354   43297  0.353566\n",
       "7   3.999900e+04  5832   49129  0.401191\n",
       "8   4.499900e+04  5547   54676  0.446488\n",
       "9   4.999900e+04  5254   59930  0.489392\n",
       "10  5.499900e+04  5102   65032  0.531056\n",
       "11  5.999900e+04  4256   69288  0.565810\n",
       "12  6.499900e+04  4356   73644  0.601382\n",
       "13  6.999900e+04  3949   77593  0.633629\n",
       "14  7.499900e+04  3756   81349  0.664301\n",
       "15  7.999900e+04  3414   84763  0.692180\n",
       "16  8.499900e+04  3326   88089  0.719341\n",
       "17  8.999900e+04  2643   90732  0.740923\n",
       "18  9.499900e+04  2678   93410  0.762792\n",
       "19  9.999900e+04  2223   95633  0.780945\n",
       "20  1.049990e+05  2606   98239  0.802226\n",
       "21  1.099990e+05  1838  100077  0.817235\n",
       "22  1.149990e+05  1986  102063  0.833453\n",
       "23  1.199990e+05  1464  103527  0.845408\n",
       "24  1.249990e+05  1596  105123  0.858441\n",
       "25  1.299990e+05  1327  106450  0.869278\n",
       "26  1.349990e+05  1253  107703  0.879510\n",
       "27  1.399990e+05  1140  108843  0.888819\n",
       "28  1.449990e+05  1119  109962  0.897957\n",
       "29  1.499990e+05   920  110882  0.905470\n",
       "30  1.549990e+05  1143  112025  0.914803\n",
       "31  1.599990e+05   805  112830  0.921377\n",
       "32  1.649990e+05   731  113561  0.927347\n",
       "33  1.699990e+05   575  114136  0.932042\n",
       "34  1.749990e+05   616  114752  0.937072\n",
       "35  1.799990e+05   570  115322  0.941727\n",
       "36  1.849990e+05   502  115824  0.945826\n",
       "37  1.899990e+05   364  116188  0.948799\n",
       "38  1.949990e+05   432  116620  0.952327\n",
       "39  1.999990e+05   378  116998  0.955413\n",
       "40  2.499990e+05  2549  119547  0.976229\n",
       "41           inf  2911  122458  1.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hinc\n",
    "df = hinc.ReadData()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the CDF looks like on a linear scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ps = df.income.values, df.ps.values\n",
    "cdf = thinkstats2.Cdf(xs, ps, label='data')\n",
    "cdf_log = thinkstats2.Cdf(np.log10(xs), ps, label='data')\n",
    "    \n",
    "# linear plot\n",
    "thinkplot.Cdf(cdf) \n",
    "thinkplot.Config(xlabel='household income',\n",
    "                   ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check whether a Pareto model describes the data well, I plot the CCDF on a log-log scale.\n",
    "\n",
    "I found parameters for the Pareto model that match the tail of the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ys = thinkstats2.RenderParetoCdf(xmin=55000, alpha=2.5, \n",
    "                                     low=0, high=250000)\n",
    "\n",
    "thinkplot.Plot(xs, 1-ys, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf, complement=True) \n",
    "thinkplot.Config(xlabel='log10 household income',\n",
    "                 ylabel='CCDF',\n",
    "                 xscale='log',\n",
    "                 yscale='log', \n",
    "                 loc='lower left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the lognormal model I estimate mu and sigma using percentile-based statistics (median and IQR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.740354793159152 0.35\n"
     ]
    }
   ],
   "source": [
    "median = cdf_log.Percentile(50)\n",
    "iqr = cdf_log.Percentile(75) - cdf_log.Percentile(25)\n",
    "std = iqr / 1.349\n",
    "\n",
    "# choose std to match the upper tail\n",
    "std = 0.35\n",
    "print(median, std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the distribution, and fitted model, look like on a log-x scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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czLrCn3Vmdv5YBCciI7fnILWUmo1k2PZaUwg//P+GYBGcnxP0LRwFfM3McPcbog9RRPZGg9RkNA319eGjwJnu/rC773L3ne6+FnhfeExEqkyD1GQ0DZUUGtz9tYGFYVlDFAGJyMgUB6mVT4kNGqQm+2aou4969vGYiIyhQsHJZDRITfbfUEnhIDN7sUK5AX8UQTwiMkIDm40SiYTuNpJ9NpxpLuYArw8oPwDYEElEIjIiAwepzZkzR3cbyT4b6p3zdaDd3X9X/gN0h8dEpIo0SE1G21BJYbG779F85O7rgMWRRCQiw6JBahKFoZqPavZyTF9HRMZI+ViEor6+XNiXENAgNRkNQ72DnjGzjwwsNLMPA89GE5KIDDQwIRSnxC5KJJLUTtvbdziR4RmqpvA3wN1m9gF2J4HlQAo4M8rARGS38oRQaZBaQ12txiPIqBhqQrzNwHFmdiJwSFj8g3BUs4hUwX9/6Ty2bt1a2p8/f746l2XUDFVTAMDdHwYejjgWERlCoVBg27Ztpf3GxkYlBBlV6pUSmSDcIZPJ4r57JbVZs2ZVOSqZbJQURCaIXK6PQkGD1CRakb6jzOxUM/uVma03s8v3ct7ZZuZmpp4ykQoKhUK/u41mzpxJTY3uNpLRF1lSMLM4cA1wGrAMOM/MllU4rx74BPBUVLGITGTuTiazezxCKpVSs5FEZlgdzfvoGGC9u78KYGa3ASuBlwec9yXgq8CnIoxFZFyrNDitKJvNUigUSvtz5szBzMYyPJlComw+mk//ifRaw7ISMzsSWOju90UYh8i4N1hCyOcL9PXtLq+fXks6nR7L0GSKiTIpVPoq46WDZjGCSfX+bsgnMrskXBt6Xfn92SKTRaWE4O79prGYVpPiAyveNpZhyRQUZfNRK7CwbH8B/afbricYEPdIWBWeC6wxsxXhhHsl7r4KWAWwfPlyR2QSu+sbfwnAtm3b2LlzJwBmxqJFi0gmtbSmRCvKmsIzwFIzW2JmKeBcYE3xYLjmc5O7L3b3xcCTwB4JQWQq6u7uLiUEgKamJiUEGRORJQV3zwGXAQ8CrwB3uPtLZvZFM1sR1euKTHT5fD6cEjtQW1tLQ4OWRJexEWXzEe5+P3D/gLIrBjn3hChjEZkotm7dSi6XAyAWi9HS0qK7jWTMaDikyDiSy+Xo7Ows7be0tJBIRPrdTaQfJQWRcaJQKJDJ7B61XF9fT11dXRUjkqlISUFkHAgmu9u9RkIikaC5ubm6QcmUpHqpSMT2Nlq5qK+v/6jluXPnarI7qQq960QiNlRCyOfzpVHLqWSCWbNmabI7qRolBZGI7S0hBJPdBf0IqWSC9550CDNnzhyr0ET2oOYjkTFUHK0MQULYvHlz6W6jWCzGwoULdfupVJVqCiJV0tHR0e/20+bmZo1alqpTUhCpgmw2S/nkjg0NDdTX11cxIpGAkoLIGCsUCmzatKnfWstNTU1VjkokoKQgMsa2bdtWWlrTzHT7qYwreieKjKGOjg7a29tL+01NTVo0R8YVJQWRMVIoFPrNflpXV6fZT2Xc0S2pIvtoOCOVi4LxCJl+/Qia/VTGI9UURPbR8BNCcLdRIh78uakfQcYzvStF9tFwEgIE02HHDM44/s2A+hFkfFPzkcgoKB+pXC6TydDa2lpqNqqvr1c/goxrqimIRCSfz7Nx48ZSQkilUjQ3N6sfQcY1JQWRCLg7mzZt6respvoRZCLQO1QkAm1tbfT09JT258yZQyqVqmJEIsOjpCAyyjo7O9m5c2dpf+bMmUyfPr2KEYkMn5KCyCjKZDJs3ry5tF9bW8usWbOqGJHIyCgpiIySXC7Xr2M5mUwyZ84cdSzLhKKkIDIKKnUsz5s3j3g8XuXIREZG4xREQiOZtqKcO2zZsoXe3t5S2dy5c9WxLBOSagoioX1JCAAxczo6Okr7TU1N1NbWjmZoImNGNQWR0L4khHgMTl6+pLTf0NDAjBkzRjMskTGlpCBSwWDTVpTr6elhw4YNpY7ladOmacSyTHhqPhLZB9lsdo8pLObOnauEIBOekoLICOVyOTZs2EChUAAgHo/rTiOZNJQUREYgn8+zYcOG0q2nZsa8efNIJpNVjkxkdESaFMzsVDP7lZmtN7PLKxz/pJm9bGYvmtlDZnZAlPGI7I9CocCmTZvIZrOlsrlz51JTU1PFqERGV2RJwcziwDXAacAy4DwzWzbgtOeA5e5+GPA94KtRxSOyP9ydzZs395vkrqWlRXMayaQTZU3hGGC9u7/q7lngNmBl+Qnu/rC7d4e7TwILIoxHZJ8UE0JXV1epbPbs2VosRyalKJPCfOD1sv3WsGwwHwYeiDAekRFzd7Zt20ZnZ2epbObMmcycObOKUYlEJ8pxCpXuzfOKJ5p9EFgOvHOQ45cAlwAsWrRotOIT2atiQti1a1eprKGhQbOeyqQWZVJoBRaW7S8ANgw8yczeBXwWeKe7Zyo9kbuvAlYBLF++vGJiEYF9n79oIHenra2tX0Koq6vT4DSZ9KJsPnoGWGpmS8wsBZwLrCk/wcyOBP4DWOHuWyKMRaaI0UgI6VSCtra2fgvl1NXVaRpsmRIiSwrungMuAx4EXgHucPeXzOyLZrYiPO1rQB1wp5k9b2ZrBnk6kWEZjYRw2nEH9ksI06dPV0KQKSPSuY/c/X7g/gFlV5RtvyvK15epbTjzF5Vzd7Zu3Up7e3upbPr06Zq+QqYUTYgnQpAQtmzZ0m8KbDUZyVSkpCBTXnGkcnd3d6msvr6elpYWJQSZcpQUZErL5/Ns3Lix36ppDQ0NustIpiwlBZmy+vr62LhxY7+5jGbOnMmsWbOUEGTKUlKQKam3t5eNGzeSz+dLZU1NTTQ2NlYxKpHqU1KQKaerq4tNmzaVFsgxM1paWqivr69yZCLVp6QgU4a7s2PHDrZv314qi8VizJs3j2nTplUxMpHxY1Ikhb6+PlpbW/t1Fk5FNTU1LFiwQAu+VFAoFNiyZUu/ie2SySTz5s0jlUpVMTKR8WVSJIXW1lbq6+tZvHjxlO0gLM7V09raypIlS6odzoiM1nxFg8lms3ssjjNt2jTmzp2rJTRFBpgUy3H29vYye/bsKZsQIGgXnz179oSsLUWRENKpoLbU0dHB66+/3i8hzJgxgze84Q1KCCIVTIqaAjClE0LRRL0GUSSEPz/lKLZu3dpvllMzo7m5WYvjiOzFpEkKk8nixYtZt24dTU1N+3XORDTS+YoqyWQybN68uV9CSCaTzJ07l3Q6vd/PLzKZKSnIpOHu7Nq1i7a2ttLtphBMatfS0qLmIpFhmBR9CuPBa6+9xlve8hYuvvhiDjnkED7wgQ/w4x//mLe//e0sXbqUp59+mu3bt/Pe976Xww47jGOPPZYXX3wRgLa2Nt7znvdw5JFHcumll/b7QLvppps45phjOOKII7j00kv7DbaS3bLZLH/4wx/Ytm1bv/EHzc3N6lAWGYFJV1NYv359ZM/9pje9acjXvvPOO1m1ahVvfetbueWWW/jZz37GmjVr+Od//mcWLlzIkUceyerVq1m7di3nn38+zz//PF/4whc4/vjjueKKK/jBD37AqlWrAHjllVe4/fbbeeyxx0gmk3z0ox/l5ptv5vzzz4/sd5xo3J2dO3eyffv2fsk0nU4zZ84c3W4qMkKTLilU05IlSzj00EMBOPjggzn55JMxMw499FBee+01fve733HXXXcBcNJJJ5WWe3z00Uf5/ve/D8AZZ5xRWhT+oYce4tlnn+Wtb30rAD09PbS0tFThNxufenp62Lp1a787i0DzF4nsDyWFUVTeiRmLxUr7sViMXC5HIrHn5S5+cFX6AHN3LrjgAr7yla9EFPHElMvlaGtr67f2AQTXv6WlRZ3JIvth0iWFoZp4qukd73gHN998M//4j//II488QlNTEw0NDaXyz33uczzwwAPs2LEDgJNPPpmVK1fyt3/7t7S0tLB9+3Y6Ojo44IADqvybVEehUGDnzp3s2LGjX1ORmTFr1iwaGxtVOxDZT5MuKYxnV155JRdddBGHHXYYtbW1XH/99QB8/vOf57zzzuOoo47ine98J4sWLQJg2bJl/NM//RPvec97KBQKJJNJrrnmmimXFNydjo4Otm/fTi6X63ds+vTpNDc3V6yFicjIWfk3rolg+fLlvm7dun5lr7zyCgcddFCVIhpfRutaRD31xGDKxym4O52dnWzfvp2+vv5xpFIpmpqaqK2tHdP4RCYqM3vW3ZcPdZ6+XklF1UgIxakpijWDHTt27JEM4vE4s2bNoqGhQU1FIhFQUpCKqpEQ3n/KkezYsYNdu3bt0UwUi8VobGyksbGRWEzDa0SioqQgQxqNqSf2JpvNsmvXLtrb22lra+t3LBaLMWPGDBobGzUATWQMKClIVRQKBTo7O2lvb684s2s8HqexsZGGhgYlA5ExpKQgY8bd6enpoaOjg87OTird5JBKpWhsbKSurk7NRCJVoKQgkSomgs7OTrq6ugadu6muro6GhgamTZumDmSRKlJSiMiVV15JXV0dn/rUpyoeX716NQceeCDLli0b48iil8vl6O7upquri56eHgqFQsXzUqkU9fX11NfXa5yByDihv8QxsLO9m+27uiiUNZfccPNtnHjSu0nVje56CFu3d/C5v752VJ9zKPl8np6entLPwLmIysXj8VIiSKVSqhWIjDNKCqPoy1/+MjfccAMLFy6kubmZo48+mu985zt88+pv0deXZdEBS/i3f7+aV156ibU/epBnnnycb139da6+9rs8+fhPuf2Wm/qdN21a9QdmFccOFLk72WyW3t5eMpkMvb29e00CAIlEgrq6OqZPn05NTY0Sgcg4NumSwvsi/Ja8t1szn332WW677Taee+45crkcRx11FEcffTRnnXUWJ56yEoD/+7WvcOdtt3D+RRdz0rtP4cST3s1pZ/wZAA0NDZxz3of2OK+aUskEZ558KDt37iSbzZLJZMhmsxU7iMuZGTU1NdTW1jJ9+nSSyaQSgcgEMemSQrX89Kc/5cwzzyxNu7BixQoAfvnLX/KpT/8D7e276O7q4owzTudNi1pomF7DvOYZvGlRMBX2T377Cp+87CPs3LmTzs5OTjnllNKxkejrahv2uAJ3J5/Pk8vl6Ovr6/eTzWZLfQHbtm3b6/OYGel0upQIampqdOeQyAQVaVIws1OBbwBx4D/d/V8GHE8DNwBHA23AOe7+WpQxRanSt+ELL7yQq779XQ5adjB33Xkbr/zi5xUfe+GFF7J69WoOP/xwrrvuOh555JF9jqP4YT/wJ5fLkcvl+m3vy9xXiUSilARqampIp9NKAiKTRGRJwcziwDXAu4FW4BkzW+PuL5ed9mFgh7u/yczOBf4VOGd/Xjfq0beDecc73sGFF17I5ZdfTi6X49577+XSSy+lo6ODlpYW+vr6WLP6Lpa+cQkA9fX1tLe3lz6UOzo6mDNnDplMhptuuon58+fvcftm+Qd4cXvgv319ffzmN78Zld/JzEilUqWfdDpNOp3WYDKRSSzKmsIxwHp3fxXAzG4DVgLlSWElcGW4/T3gajMzH+WpWwuFwojXNh5pCIcccghnn302hx9+OIsWLeK4444jl8vx+c9/nvetPI358xdw4JsPIp8POmnPPPNMPvaxj3HVVVdxyy23cMUVV3DssceyaNEiDj74YDo7O/eYDC4KsViMRCJBMpns95NKpYjH4+oLEJliIps628zOBk5194vD/Q8Bf+zul5Wd88vwnNZw/zfhOYM2Yu/L1NmFQmHIO2Si9PtNO0vbi+Y2Rvpav/71r6mtrSUej1f8SSQSJBIJ4vE4yWRSzT4iU8R4mDq70lfMgRloOOdgZpcAlwClBWgmk0rfxgeWle9XWsKzuJ1IJHjjG9+ob/gisk+iTAqtwMKy/QXAhkHOaTWzBDAD2D7widx9FbAKgprCSAMxM5LJ5NAn7oPhfPiWfxsfuH7waH94m5kSgojssyiTwjPAUjNbAvwBOBf4iwHnrAEuAJ4AzgbWjnZ/AgQflNXsHN2XW0tFRKohsqTg7jkzuwx4kOCW1O+6+0tm9kVgnbuvAf4LuNHM1hPUEM7dj9eb8t+QJ9rSqiIy/kQ6TsHd7wfuH1DeYhVEAAAKpUlEQVR2Rdl2L/D+/X2dmpoa2tramD179pRNDO5OW1sbNTU11Q5FRCawSTGiecGCBbS2trJ169Zqh1JVNTU1LFiwoNphiMgENimSQjKZZMmSJdUOQ0RkwtNN6iIiUqKkICIiJUoKIiJSEtk0F1Exs63A7/bx4U3A3ueBrg7FNTKKa+TGa2yKa2T2J64D3L15qJMmXFLYH2a2bjhzf4w1xTUyimvkxmtsimtkxiIuNR+JiEiJkoKIiJRMtaSwqtoBDEJxjYziGrnxGpviGpnI45pSfQoiIrJ3U62mICIiezEpkoKZ1ZjZ02b2gpm9ZGZfqHDOhWa21cyeD38uLjt2gZn9b/hzwRjH9fWymH5tZjvLjuXLjq0ZrbjKnj9uZs+Z2X0VjqXN7HYzW29mT5nZ4rJjnwnLf2Vmp4xxXJ80s5fN7EUze8jMDig7Vs3rNebvr2HGVZX3l5m9Zma/CJ97XYXjZmZXhe+jF83sqLJjkV2vYcT1gTCeF83scTM7fLiPjTiuE8xsV9n/1xVlx04N/xbXm9nl+x2Mu0/4H4IV3OrC7STwFHDsgHMuBK6u8NhZwKvhvzPD7ZljFdeA8z9OMMV4cb8z4uv2SeAW4L4Kxz4KXBtunwvcHm4vA14A0sAS4DdAfAzjOhGoDbf/qhjXOLheY/7+Gk5c1Xp/Aa8BTXs5fjrwQPg3cizw1Fhcr2HEdVzx9YDTinEN57ERx3XCIO+7ePg3+EdAKvzbXLY/sUyKmoIHOsPdZPgz3M6SU4Afuft2d98B/Ag4tUpxnQfcOhqvPRQzWwCcAfznIKesBK4Pt78HnGxmFpbf5u4Zd/8tsB44ZqzicveH3b073H2SYEW/yA3jeg0msvfXPsQ1Zu+vYVgJ3BD+jTwJNJrZPCK+XkNx98fD14UxfH/th2OA9e7+qrtngdsIru0+mxRJAUpV6OeBLQRvqqcqnPa+sFr4PTMrLhU6H3i97JzWsGws4yJsBlkCrC0rrjGzdWb2pJm9d7RiCv078PdAYZDjpevi7jlgFzCbiK/XMOIq92GCb5tF1bxeUIX31zDjqsb7y4EfmtmzFqyxPtBg1yXq6zVUXOUGvr9G8tgo4nqbBU3RD5jZwWHZqF+vSTF1NoC754EjzKwRuNvMDnH3X5adci9wq7tnzOwvCb4Fn0RQfd3j6cYwrqJzge+F5xctcvcNZvZHwFoz+4W7/2Z/YzKzPwW2uPuzZnbCYKdVKPO9lO+3YcZVPPeDwHLgnWXF1bxeY/7+Gsn1YgzfX6G3h8/dAvzIzP6fuz9aHn6Fx0T6/hpmXEFwZicSJIXjR/rYiOL6OcE0FZ1mdjqwGlhKBNdr0tQUitx9J/AIA6qc7t7m7plw9zvA0eF2K7Cw7NQFwIaxiqvMuQyo2rv7hvDfV8PHHjlK4bwdWGFmrxFUN08ys5sGnFO6LmaWAGYQLJka5fUaTlyY2buAzwIryv5Pq3q9qvT+Gtb1Co3l+6v8ubcAd7NnE+Ng1yXSv8dhxIWZHUbQHLfS3dtG8tio4nL39mJTtAcrWibNrIkortdodJJU+wdoBhrD7WnAT4E/HXDOvLLtM4Enw+1ZwG8JOrVmhtuzxiqu8NibCTqarKxsJpAOt5uA/2U/O5AGifEEKndgfYz+Hc13hNsH07+j+VVGuaN5iLiOJOhYWzqgvNrXa8zfX8OJqxrvL2A6UF+2/Thw6oBzzqB/R/PTUV+vYca1iKCf7LiRPjbiuOYW//8IEsbvw2uXCP8Gl7C7o/ng/YlnsjQfzQOuN7M4Qe3nDne/z8y+CKxz9zXAJ8xsBZAj+MZ7IYC7bzezLwHPhM/1RXffPoZxQdABeJuH/+Ohg4D/MLNC+Nh/cfeXRymuigbE9V/AjWa2nuB6nQvg7i+Z2R3AywTX8mPev0ki6ri+BtQBdwb93vze3VdQ/etVjffXcOKCsX9/zSFoKoXgQ+sWd/+fsFkNd7+WYO320wk+gLuBi8JjUV6v4cR1BUHf2bfC83IeTEBX8bFjGNfZwF+ZWQ7oAc4N/z9zZnYZ8CDBnUjfdfeX9icYjWgWEZGSSdenICIi+05JQURESpQURESkRElBRERKlBRERKRESUEiY2adQ5816GMvC2d99HCQTrHcbJDZNcvOWWxmlUaNR26kv7OZXWlmn6pQXvF3MLM3mNn39idGkb1RUpDx6jHgXcDvBpSfRjC8fylwCfDtMY6rqtx9g7ufXe04ZPJSUpDIhd/uv2Zmv7RgzvhzwvKYmX3LgrUm7jOz+83sbAB3f87dX6vwdIPNrjlQ3My+Ez73D81sWviaR1gwAdyLZna3mc0Myx8xs+XhdlM4dQRmdrAFa2I8Hz5maVj+wbLy/wgHKBZ/3y9bMHHZk2Y2Jyw7wIL1H4rrQCyqcJ2ODh/3BMGI8krXslSDsGANh++b2f9YsPbAV8vOO9XMfh4+30Nh2SwzWx3G8GQ4nUOxtnJ9eJ1eM7OzzOyr4f/V/5hZsiy+n1gwaduDg1x3meCUFGQsnAUcARxO8O3/a+EHylnAYuBQ4GLgbcN4ruHOCrkUuMbdDwZ2Au8Ly28A/sHdDwN+AXx+iNf7S+Ab7n4EwQR8rWZ2EHAOwSRmRwB54APh+dMJprg4HHgU+EhYfjVBMjsMuBm4qsJr/TfwCXcfznUoOiKM5VDgHDNbaGbNBPMvvS+M4/3huV8Angtj+D/htSh6I8HUEyuBm4CH3f1QgtGzZ4SJ4ZvA2e5+NPBd4MsjiFMmiMkyzYWMb8cTzCCaBzab2U+At4bld7p7AdhkZg8P47mGOyvkb939+XD7WWCxmc0gmIvqJ2H59cCdQ7zeE8BnLVi34Pvu/r9mdjLBhHfPhFMTTCOYGh0gCxRXQHsWeHe4/TaCJAhwI1D6Vg9QIbYbCZrKhvKQu+8Kn+Nl4ACCOYMe9WC9C8qmiTieMDm6+1ozmx2+LsAD7t5nZr8gmC6hOIXDLwgS95uBQwhm8CQ8Z+Mw4pMJRklBxkKlD/K9le/NcGeFzJRt5wk+uPcmx+6ac02x0N1vMbOnCL5FP2jBMpsGXO/un6nwPH1lcwzlGfxvbGAiswplwzHw90zs5bn2llAzAO5eMLPy36FQ9pwvjbAWIxOQmo9kLDxK0LQRD5s23gE8DfyMYGGaWNj2fsIwnmsNcH7YT3EssMvdh/WNNfxGvcPM/iQs+hBQ/Gb+Grunuy515Fqw1sCr7n5V+NqHAQ8BZ1sw932xrb60VvQgHiecVJCgqelnA2LbCewys+PLztlXTwDvNLMlxfjC8keLz2vB+gvb3L19mM/5K6DZzN4WPj5puxd6kUlENQUZC3cTNJ+8QPDN9O/dfZOZ3QWcDPwS+DXBGtbFppBPEKwoNhd40czud/eLGWR2zRG4ALjWzGoJphwuPv7fgDvM7EP0X53sHOCDZtYHbCKctdPMPkewUlYM6CPoGB54p1S5TwDfNbNPA1sHifui8Jxuglkv94m7b7Vg9a7vh/FtIWjGuhL4bzN7keDaXTCC58yGNwFcFTY5JQhWfduvGTll/NEsqVJVZlbnwWpSswlqD293903VjktkqlJNQartPguWKk0BX1JCEKku1RRERKREHc0iIlKipCAiIiVKCiIiUqKkICIiJUoKIiJSoqQgIiIl/x/r6WYAT5dHrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ps = thinkstats2.RenderNormalCdf(median, std, low=3.5, high=5.5)\n",
    "thinkplot.Plot(xs, ps, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf_log) \n",
    "thinkplot.Config(xlabel='log10 household income',\n",
    "                 ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My conclusions based on these figures are:\n",
    "\n",
    "1) The Pareto model might be a reasonable choice for the top\n",
    "   10-20% of incomes.\n",
    "\n",
    "2) The lognormal model captures the shape of the distribution better,\n",
    "   with some deviation in the left tail.  With different\n",
    "   choices for sigma, you could match the upper or lower tail, but not\n",
    "   both at the same time.\n",
    " \n",
    "In summary I would say that neither model captures the whole distribution,\n",
    "so you might have to \n",
    "\n",
    "1) look for another analytic model, \n",
    "\n",
    "2) choose one that captures the part of the distribution that is most \n",
    "   relevent, or \n",
    "\n",
    "3) avoid using an analytic model altogether."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
